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Given the theoretical hardness of classical planning, the chance of developing practically usable solution algorithms appears slim. However, modern planning systems developed since the 1990s such as Graphplan [14], SATPLAN [75-78], HSP [16] and FF [68] have demonstrated their ability to solve planning tasks of considerable size. Their efficiency, or - put a bit more carefully - their perceived efficiency is somewhat at odds with the theoretical hardness of planning. To understand why we can observe such good planner performance, we must consider the methods with which the performance of planning systems is evaluated. This is the topic of the following Sect. 1.1, which will lead us to a discussion of planning benchmarks in Sect. 1.2 and their theoretical properties in Sect. 1.3. In the penultimate Sect. 1.4, we briefly introduce a set of standard benchmarks. The chapter concludes with an overview of Part I in Sect. 1.5. [ABSTRACT FROM AUTHOR]

Having established transportation planning and route planning as common concepts in the IPC benchmark suite, we now take an in-depth look at the complexity of solving such planning problems. In the following Sect. 4.1, we introduce the general Transport and Route domains. In Sect. 4.2, we present some first upper bounds and general reductions. Section 4.3 discusses the plan existence problem and shows restricted fuel to be a major source of hardness for transportation and route planning. Section 4.4 shows that optimal planning or polynomial-time approximation schemes are beyond reach even for quite restricted domain variants. On the positive side, we see in Sect. 4.5 that constant-factor approximations are feasible in many cases. However, in many others they are not, which is shown in Sect. 4.6. Finally, Sect. 4.7 summarizes the findings of this chapter, and Sect. 4.8 puts them into a wider context. [ABSTRACT FROM AUTHOR]

This chapter introduces the main ideas and concepts underlying the Fast Downward planner and puts them into the context of earlier research. In Sect. 8.1, we present a simple example to illustrate key aspects of the planner such as the exploitation of causal dependencies and the use of multi-valued state variables. Section 8.2 puts these ideas into the context of earlier research. The concluding Sect. 8.3 presents a bird's-eye view of the overall planner architecture and provides an outlook on the chapters to come. [ABSTRACT FROM AUTHOR]

I analyze some of the attacks against the Physical Symbol System Hypothesis—attacks based on the presumed need for symbol-grounding and non-symbolic processing for intelligent behavior and on the supposed non-computational and "mindless" aspects of brains. [ABSTRACT FROM AUTHOR]

When Kurt Gödel layed the foundations of theoretical computer science in 1931, he also introduced essential concepts of the theory of Artificial Intelligence (AI). Although much of subsequent AI research has focused on heuristics, which still play a major role in many practical AI applications, in the new millennium AI theory has finally become a full-fledged formal science, with important optimality results for embodied agents living in unknown environments, obtained through a combination of theory à la Gödel and probability theory. Here we look back at important milestones of AI history, mention essential recent results, and speculate about what we may expect from the next 25 years, emphasizing the significance of the ongoing dramatic hardware speedups, and discussing Gödel-inspired, self-referential, self-improving universal problem solvers. [ABSTRACT FROM AUTHOR]

Evolutionary robotics is a methodology for the creation of auto- nomous robots using evolutionary principles. Humanoid robotics is concerned specifically with autonomous robots that are human-like in that they mimic the body or aspects of the sensory, processing and/or motor functions of humans to a greater or lesser degree. We investigate how these twin strands of advanced research in the field of autonomous mobile robotics have progressed over the last decade or so, and their current recent convergence in the new field of evolutionary humanoid robotics. We describe our current work in the evolution of controllers for bipedal locomotion in a simulated humanoid robot using an accurate physics simulator, and briefly discuss the effects of changes in robot mobility and of environmental changes. We then describe our current work in the implementation of these simulated robots using the Bioloid robot platform. We conclude with a look at possible visions for the future. [ABSTRACT FROM AUTHOR]

Artificial Intelligence was born in 1956 as the off-spring of the newly-created cognitivist paradigm of cognition. As such, it inherited a strong philosophical legacy of functionalism, dualism, and positivism. This legacy found its strongest statement some 20 years later in the physical symbol systems hypothesis, a conjecture that deeply influenced the evolution of AI in subsequent years. Recent history has seen a swing away from the functionalism of classical AI toward an alternative position that re-asserts the primacy of embodiment, development, interaction, and, more recently, emotion in cognitive systems, focussing now more than ever on enactive models of cognition. Arguably, this swing represents a true paradigm shift in our thinking. However, the philosophical foundations of these approaches — phenomenology — entail some far-reaching ontological and epistemological commitments regarding the nature of a cognitive system, its reality, and the role of its interaction with its environment. The goal of this paper is to draw out the full philosophical implications of the phenomenological position that underpins the current paradigm shift towards enactive cognition. [ABSTRACT FROM AUTHOR]

This paper supports the view that the ongoing shift from orthodox to embodied-embedded cognitive science has been significantly influenced by the experimental results generated by AI research. Recently, there has also been a noticeable shift toward enactivism, a paradigm which radicalizes the embodied-embedded approach by placing autonomous agency and lived subjectivity at the heart of cognitive science. Some first steps toward a clarification of the relationship of AI to this further shift are outlined. It is concluded that the success of enactivism in establishing itself as a mainstream cognitive science research program will depend less on progress made in AI research and more on the development of a phenomenological pragmatics. [ABSTRACT FROM AUTHOR]

Embodied artificial intelligence is based on the notion that cognition and action emerge from interactions between brain, body and environment. This chapter sketches a set of foundational principles that might be useful for understanding the emergence ("discovery") of intelligence in biological and artificial embodied systems. Special emphasis is placed on information as a crucial resource for organisms and on information theory as a promising descriptive and predictive framework linking morphology, perception, action and neural control. [ABSTRACT FROM AUTHOR]

This paper exposes and discusses the concept of 'networked embodied cognition', based on natural embodied neural networks, with some considerations on the nature of natural collective intelligence and cognition, and with reference to natural biological examples, evolution theory, neural network science and technology results, network robotics. It shows that this could be the method of cognitive adaptation to the environment most widely used by living systems and most fit to the deployment of artificial robotic networks. Some preliminary ideas about the development of a quantitative framework are shortly discussed. On the basis of the work of many people a few approximate simple quantitative relations are derived between information metrics of the phase space behavior of the agent dynamical system and those of the cognition system perceived by an external observer. [ABSTRACT FROM AUTHOR]

In this paper we have investigated a group of multi agent systems (MAS) in which the agents change their environment and this change has the potential to trigger behaviors in other agents of the group in another time or another position in the environment. The structure makes it possible to conceptualize the group as a super organism incorporating the agents and the environment such that new behaviors are observed from the whole group as a result of the specific distribution of agents in that environment. This distribution exists in many aspects like a super memory (or even a super brain) that exists in the environment and is not limited to memories of the individuals. There is a distributed decision making that is done by the group of agents which, in a higher level consists of both individual and group decision makings, and can be viewed as emergent rather than consciously planned. As the agents change the environment, they decrease the error for the group and hence a distributed learning is also forming between the agents of the group. This implicit learning is related to the implicit memory existing in the environment. These two interpretations of memory and learning are assessed with experimental results where two robots perform a task while they are not aware of their global behavior. [ABSTRACT FROM AUTHOR]

This article discusses the issues of adaptive autonomous navigation as a challenge of artificial intelligence. We argue that, in order to enhance the dexterity and adaptivity in robot navigation, we need to take into account the decentralized mechanisms which exploit physical system-environment interactions. In this paper, by introducing a few underactuated locomotion systems, we explain (1) how mechanical body structures are related to motor control in locomotion behavior, (2) how a simple computational control process can generate complex locomotion behavior, and (3) how a motor control architecture can exploit the body dynamics through a learning process. Based on the case studies, we discuss the challenges and perspectives toward a new framework of adaptive robot control. [ABSTRACT FROM AUTHOR]

This paper introduces our robotic case study which is intended to intensively investigate the neural-body coupling, i.e., how the task distribution between control and mechanical systems should be achieved, so as to emerge useful functionalities. One of the significant features of this case study is that we have employed a collective behavioral approach. More specifically, we have focused on an "embodied" coupled nonlinear oscillator system by which we have generated one of the most primitive yet flexible locomotion, i.e., amoeboid locomotion, in the hope that this primitiveness allows us to investigate the neural-body coupling effectively. Experiments we have conducted strongly support that there exists an "ecologically-balanced" task distribution, under which significant abilities such as real-time adaptivity emerge. [ABSTRACT FROM AUTHOR]

A new approach of integrating biological microorganisms such as bacteria to an inorganic robot body for propulsion in low velocity or stagnant flow field is proposed in this paper with the ultimate goal of fabricating a few hundreds of micrometer size swimming robots. To show the feasibility of this approach, Serratia marcescens bacteria are attached to microscale objects such as 10 micron polystyrene beads by blotting them in a bacteria swarm plate. Randomly attached bacteria are shown to propel the beads at an average speed of approximately 15 μm/sec stochastically. Using chemical stimuli, bacteria flagellar propulsion is halted by introducing copper ions into the motility medium of the beads, while ethylenediaminetetraacetic acid is used to resume their motion. Thus, repeatable on/off motion control of the bacteria integrated mobile beads was shown. On-board chemical motion control, steering, wireless communication, sensing, and position detection are few of the future challenges for this work. Small or large numbers of these microrobots can potentially enable hardware platforms for self-organization, swarm intelligence, distributed control, and reconfigurable systems in the future. [ABSTRACT FROM AUTHOR]

Compressing real-time input through bandwidth constrained connections has been studied within robotics, wireless sensor networks, and image processing. When there are bandwidth constraints on real-time input the amount of information to be transferred will always be greater than the amount that can be transferred per unit of time. We propose a system that utilizes a local diffusion process and a reinforcement learning-based memory system to establish a real-time prediction of an entire input space based upon partial observation. The proposed system is optimized for dealing with multi-dimension input spaces, and maintains the ability to react to rare events. Results show the relation of loss to quality and suggest that at higher resolutions gains in quality are possible. [ABSTRACT FROM AUTHOR]

This case study demonstrates how the synthesis and the analysis of minimal recurrent neural robot control provide insights into the exploration of embodiment. By using structural evolution, minimal recurrent neural networks of general type were evolved for behavior control. The small size of the neural structures facilitates thorough investigations of behavior relevant neural dynamics and how they relate to interactions of robots within the sensorimotor loop. We argue that a clarification of dynamical neural control mechanisms in a reasonable depth allows quantitative statements about the effects of the sensorimotor loop and suggests general qualitative implications about the embodiment of autonomous robots and biological systems as well. [ABSTRACT FROM AUTHOR]

Although the definition and measurement of intelligence is clearly of fundamental importance to the field of artificial intelligence, no general survey of definitions and tests of machine intelligence exists. Indeed few researchers are even aware of alternatives to the Turing test and its many derivatives. In this paper we fill this gap by providing a short survey of the many tests of machine intelligence that have been proposed. [ABSTRACT FROM AUTHOR]

Empirical evidence indicates that anticipatory representations grounded in the sensorimotor neural apparatus are crucially involved in several low and high level cognitive functions, including attention, motor control, planning, and goal-oriented behavior. A unitary theoretical framework is emerging that emphasizes how simulative capabilities enable social abilities, too, including joint attention, imitation, perspective taking and communication. We argue that anticipation will be a key element for bootstrapping high level cognitive functions in cognitive robotics, too. We thus propose the challenge of understanding how anticipatory representations, that serve for coordinating with the future and not only with the present, develop in situated agents. [ABSTRACT FROM AUTHOR]

Computers have become an essential part of modern life, providing services in a multiplicity of ways. Access to these services, however, comes at a price: human attention is bound and directed toward a technical artifact in a human-machine interaction setting at the expense of time and attention for other humans. This paper explores a new class of computer services that support human-human interaction and communication implicitly and transparently. Computers in the Human Interaction Loop (CHIL), require consideration of all communication modalities, multimodal integration and more robust performance. We review the technologies and several CHIL services providing human-human support. Among them, we specifically highlight advanced computer services for cross-lingual communication. [ABSTRACT FROM AUTHOR]

The chapter discusses a recent paradigm shift in the field of Artificial Intelligence regarding the nature of human intelligence and its implications for the design and development of intelligent robots. It will be argued that social intelligence is not a mere ‘add-on' to intelligent robot behaviour for the practical purpose of enabling the robot to interact smoothly with other robots or people, but that social intelligence might be a stepping stone towards more human-like, embodied artificial intelligence. The argument is supported by discussions in primatology highlighting the social origins of primate intelligence. The chapter also discusses challenges and opportunities provided by socially intelligent robots, with implications for our future. [ABSTRACT FROM AUTHOR]

Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers have started to address the problem of designing intrinsic motivation systems. A robot controlled by such systems is able to autonomously explore its environment not to fulfil predefined tasks but driven by an incentive to search for situations where learning happens efficiently. In this paper, we present the origins of these intrinsically motivated machines, our own research in this novel field and we argue that intrinsic motivation might be a crucial step towards machines capable of life-long learning and open-ended development. [ABSTRACT FROM AUTHOR]

This paper focuses on designing a goal based rational component of a believable agent which has to interact with facial expressions with humans in communicative scenarios like teaching. One of the main concerns of the proposed model is to define interactions among rationality, personality and emotion in order to fulfill the idea of making rational decisions with emotional regulation. Our research aims are directed towards improving decision making process by means of applying Data Fusion techniques, especially Ordered Weighted Averaging (OWA) operator as a goal selection mechanism. Also the issue of obtaining weights for OWA aggregation is discussed. Finally the suggested algorithm is tested and results are provided with a real benchmark. [ABSTRACT FROM AUTHOR]

The problem of consciousness has been divided by philosophers into the problem of Access Consciousness and the problem of Phenomenal Consciousness or "raw feel". In this chapter it is suggested that Access Consciousness is something that we can logically envisage building into a robot because it is a cognitive capacity giving rise to behaviors or behavioral tendencies or potentials. A few examples are given of how this is being done in current research. On the other hand, Phenomenal Consciousness or "raw feel" is problematic, since we do not know what we really mean by "feel". It is suggested that three main properties are what characterize feel: the fact that feels are different from each other, that there is structure in these differences, and that feels have sensory presence. It is then shown how, by taking the sensorimotor approach [24], [27] it is possible to account for these properties in a natural way and furthermore to make counter-intuitive empirical predictions which have recently been confirmed. In conclusion it is claimed that when we take the sensorimotor approach to feel, building raw feel into a robot becomes a theoretical possibility, even if we are a long way from actually attaining it. [ABSTRACT FROM AUTHOR]

"Without embodiment artificial intelligence is nothing." Algorithms in the field of artificial intelligence are mostly tested on a computer instead of testing on a real platform. Our anthropomorphic robot ZAR5 (in German Zwei-Arm-Roboter in the 5th version) is the first biologically inspired and completely artificial muscle driven robot torso that can be fully controlled by a data suit and two five finger data gloves. The underlying biological principles of sensor technology, signal processing, control architecture und actuator technology of our robot platform meet the requirements of biological based technical realization and support a distributed programming and control as well as an online self-adaptation and relearning processing. The following elaboration focuses on biological inspiration for the embodiment of artificial intelligence, gives a short insight into technical realisation of a humanoid robot, which is of high importance in this context, and accentuates highlights relating to a possible paradigm shift in artificial intelligence. [ABSTRACT FROM AUTHOR]

This paper describes a multi-disciplinary initiative to promote collaborative research in enactive artificial cognitive systems by developing the iCub : a open-systems 53 degree-of-freedom cognitive humanoid robot. At 94 cm tall, the iCub is the same size as a three year-old child. It will be able to crawl on all fours and sit up, its hands will allow dexterous manipulation, and its head and eyes are fully articulated. It has visual, vestibular, auditory, and haptic sensory capabilities. As an open system, the design and documentation of all hardware and software is licensed under the Free Software Foundation GNU licences so that the system can be freely replicated and customized. We begin this paper by outlining the enactive approach to cognition, drawing out the implications for phylogenetic configuration, the necessity for ontogenetic development, and the importance of humanoid embodiment. This is followed by a short discussion of our motivation for adopting an open-systems approach. We proceed to describe the iCub's mechanical and electronic specifications, its software architecture, its cognitive architecture. We conclude by discussing the iCub phylogeny, i.e. the robot's intended innate abilities, and an scenario for ontogenesis based on human neo-natal development. [ABSTRACT FROM AUTHOR]

This paper describes how industrial applications were targeted and successfully implemented by robotic manipulators that have been developed from studies in embodied artificial intelligent systems. The goal was to design mobile, flexible and self-learning manipulators that allow to perform multiple tasks with very short preparation time, a reasonable working speed and, at the same time, in a human-like manner. The advantages and disadvantages of these solutions compared to traditional industrial robot applications had to be considered continuously to concentrate on the right market segments, applications and customers. Thus, in addition to develop the appropriate requirements of real-time executions, risk analyses and usability, studies were established and implemented in collaboration with scientists, integrators and end customers. Acceptance, impacts of the revolution in personal intelligent robotics as well as challenges to overcome in the future are discussed. [ABSTRACT FROM AUTHOR]

This paper discusses the relevance of embedding dramatic scenarios and expressive language into methodologies employed in the research and development of biochemical and/or electronic sentient beings. The author demonstrates how integrating imagined modalities into current practices can afford a profound and positive effect on outcomes. [ABSTRACT FROM AUTHOR]

In the paper, a new method of decision tree learning for cost-sensitive classification is presented. In contrast to the traditional greedy top-down inducer in the proposed approach optimal trees are searched in a global manner by using an evolutionary algorithm (EA). Specialized genetic operators are applied to modify both the tree structure and tests in non-terminal nodes. A suitably defined fitness function enables the algorithm to minimize the misclassification cost instead of the number of classification errors. The performance of the EA-based method is compared to three well-recognized algorithms on real-life problems with known and randomly generated cost-matrices. Obtained results show that the proposed approach is competitive both in terms of misclassification cost and compactness of the classifier at least for some datasets. [ABSTRACT FROM AUTHOR]

In a paper [1] presented to BICS 2006, a basic methodology for microprocessor design automation using DNA sequences was proposed. A refined methodology with new schemes for traversal, encoding, recombination, and processor evaluation are proposed in this paper. Moreover concepts such as mutation, graphical decoding and environment simulation are introduced and a new technique for creating DNA based algorithms used in the mutation process is also presented. The proposed methodology is then generalized to extend its application to other domains. This paper presents a conceptual framework whose implementation aspects are still under investigation. [ABSTRACT FROM AUTHOR]

A new evolutionary-progressive method for Multiple Sequence Alignment problem is proposed. The method efficiently combines flexibility of evolutionary approach with speed and accuracy of progressive technique. The results show that the hybrid method is an interesting alternative for purely genetic or purely progressive approaches. [ABSTRACT FROM AUTHOR]

In many situations a robust design could be expensive and decision-makers need to evaluate a design that is not robust, that is, a design with a probability of satisfying the design specifications (or yield) less than 100 %. In this paper we propose a procedure for centring a design that maximises the yield, given predefined component tolerances. The hybrid approach is based on the use of Evolutionary Algorithms, Interval Arithmetic and procedures to estimate the yield percentage. The effectiveness of the method is tested on a literature case. We compare the special evolutionary strategy (1+1) with a genetic algorithm and deterministic, statistical and interval-based procedures for yield estimation. [ABSTRACT FROM AUTHOR]

In this paper we propose a new self-adaptative crossover operator for real coded evolutionary algorithms. This operator has the capacity to simulate other real-coded crossover operators dynamically and, therefore, it has the capacity to achieve exploration and exploitation dynamically during the evolutionary process according to the best individuals. In other words, the proposed crossover operator may handle the generational diversity of the population in such a way that it may either generate additional population diversity from the current one, allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions. In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator has a very suitable behavior; although, it should be noted that it offers a better behavior applied to complex search spaces than simple ones. [ABSTRACT FROM AUTHOR]

This paper provides two methodologies for forecasting time series. One of them is based on the Wavelet Analysis and the other one on the Genetic Programming. Two examples from finance domain are used to illustrate how given methodologies perform in real-life applications. Additionally application to specific classes of time series, seasonal, is discussed. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Co-evolutionary techniques for evolutionary algorithms allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. These techniques also maintain population diversity, allows for speciation and help overcoming limited adaptive capabilities of evolutionary algorithms. In this paper the idea of co-evolutionary multi-agent system with predator-prey mechanism for multi-objective optimization is introduced. In presented system the Pareto frontier is located by the population of agents as a result of co-evolutionary interactions between two species: predators and prey. Results from runs of presented system against test problem and comparison to classical multi-objective evolutionary algorithms conclude the paper. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Networks (RBFNNs) have been widely used to solve classification and regression tasks providing satisfactory results. The main issue when working with RBFNNs is how to design them because this task requires the optimization of several parameters such as the number of RBFs, the position of their centers, and their radii. The problem of setting all the previous values presents many local minima so Evolutionary Algorithms (EAs) are a common solution because of their capability of finding global minima. Two of the most important elements in an EAs are the crossover and the mutation operators. This paper presents a comparison between a non distributed multiobjective algorithm against several parallel approaches that are obtained by the specialisation of the crossover and mutation operators in different islands. The results show how the creation of specialised islands that use different combinations of crossover and mutation operators could lead to a better performance of the algorithm by obtaining better solutions. [ABSTRACT FROM AUTHOR]

In this paper we propose an immune algorithm (IA) to solve high dimensional global optimization problems. To evaluate the effectiveness and quality of the IA we performed a large set of unconstrained numerical optimisation experiments, which is a crucial component of many real-world problem-solving settings. We extensively compare the IA against several Differential Evolution (DE) algorithms as these have been shown to perform better than many other Evolutionary Algorithms on similar problems. The DE algorithms were implemented using a range of recombination and mutation operators combinations. The algorithms were tested on 13 well known benchmark problems. Our results show that the proposed IA is effective, in terms of accuracy, and capable of solving large-scale instances of our benchmarks. We also show that the IA is comparable, and often outperforms, all the DE variants, including two Memetic algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on modified genetic algorithm based on the graph representation and specialized genetic operators. Advantages of changed representation, multi-level organization as well as self-adaptive aspects of the proposed method are described. The results of algorithm usage in optimising skeletal structures are also presented. [ABSTRACT FROM AUTHOR]

Effective scheduling is of great importance to parallel programming environments. The aim is to minimize the completion time of task graphs. The completion time of a task graph is directly affected by the length of its critical path. Hence, the trend of an evolutionary approach for task graph scheduling can be biased towards reduction of the critical path. In this paper, a new genetic scheduling algorithm is presented. The algorithm, in the first priority, minimizes the critical path length of the parallel program task graph and in the second priority minimizes the inter-processor communication time. Thereby, it achieves a better scheduling in comparison with the existing approaches. [ABSTRACT FROM AUTHOR]

This paper introduces a new study in evolutionary computation technique in order to learn optimal configuration of a multilayer neural network. Inspired from thermodynamic perception, the used evolutionary framework undertakes the optimal configuration problem as a Bi-objective optimization problem. The first objective aims to learn optimal layer topology by considering optimal nodes and optimal connections by nodes. Second objective aims to learn optimal weights setting. The evaluation function of both concurrent objectives is founded on an entropy function which leads the global system to optimal generalization point. Thus, the evolutionary framework shows salient improvements in both modeling and results. The performance of the required algorithms was compared to estimations distribution algorithms in addition to the Backpropagation training algorithm. [ABSTRACT FROM AUTHOR]

The paper presents one of additional mechanisms called distributed frontier crowding which can be introduced to the Semi-Elitist Evolutionary Multi Agent System—selEMAS and which can significantly improve the quality of obtained Pareto frontier approximation. The preliminary experimental comparative studies are based on a typical multi-objective problem presenting the most important features of the proposed approach. [ABSTRACT FROM AUTHOR]

The extraction of IF-THEN rules from data is a promising task of data mining including both Artificial Intelligence and Statistics. One of the difficulties encountered is how to evaluate the relevance of the extracted rules? Many authors use statistical interestingness measures to evaluate the relevance of each rule (taken alone). Recently, few research works have done a synthesis study of the existing interestingness measures but their study presents some limits. In this paper, firstly, we present an overview of related works studying more than forty interestingness measures. Secondly, we establish a list of nineteen other interestingness measures not referenced by the related works. Then, we identify twelve semantic properties characterizing the behavior of interestingness measures. Finally, we did a theoretical study of sixty two interestingness measures by outlining their semantic properties. The results of this study are useful to the users of a data-mining system in order to help them to choose an appropriate measure. [ABSTRACT FROM AUTHOR]

A Compact Genetic Algorithm (CGA) is a genetic algorithm specially devised to meet the tight restrictions of hardware-based implementations. We propose a new mutation operator for an elitism-based CGA. The performance of this algorithm, named emCGA, was tested using a set of algebraic functions for optimization. The optimal mutation rate found for high-dimensionality functions is around 0.5%, and the low the dimension of the problem, the less sensitive is emCGA to the mutation rate. The emCGA was compared with other two similar algorithms and demonstrated better tradeoff between quality of solutions and convergence speed. It also achieved such results with smaller population sizes than the other algorithms. [ABSTRACT FROM AUTHOR]

We present an approach for automated generation of proactive aggregation protocols using Genetic Programming. First a short introduction into aggregation and proactive protocols is given. We then show how proactive aggregation protocols can be specified abstractly. To be able to use Genetic Programming to derive such protocol specifications, we describe a simulation based fitness assignment method. We have applied our approach successfully to the derivation of aggregation protocols. Experimental results are presented that were obtained using our own Distributed Genetic Programming Framework. The results are very encouraging and demonstrate clearly the utility of our approach. [ABSTRACT FROM AUTHOR]

This paper proposes an automated quantum circuit synthesis approach, using a genetic algorithm. We consider the circuit as a successive rippling of the so-called gate sections; also, the usage of a database is proposed in order to specify the gates that will be used in the synthesis process. Details are presented for an appropriate comparison with previous approaches, along with experimental results that prove the convergence and the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Efficiency of two mutation operators applied in a clonal selection based optimization algorithm AIIA for non-stationary tasks is investigated. In both operators traditional Gaussian random number generator was exchanged by α-stable random number generator and thus α became one of the parameters of the algorithm. Obtained results showed that appropriate tuning of the α parameter allows to outperform the results of algorithms with the traditional operators. [ABSTRACT FROM AUTHOR]

This paper deals with the flow-shop scheduling problem with no-store policy and minimal cycle time criterion. The model and some properties of the problem have been presented. To solve the problem, we propose new genetic algorithm equipped with auxiliary gene expression mechanism, which creates offspring using genetic information from both parents as well as asleep information from ancestors (grand- father, grand grandfather). The presented computational tests proved superiority of the proposed approach over traditional, basic GA scheme. [ABSTRACT FROM AUTHOR]

In this paper we propose a genetic and greedy algorithm combination for the optimization of the Topological Active Nets (TAN) model. This is a deformable model used for image segmentation that integrates features of region-based and edge-based segmentation techniques, being able to fit the edges of the objects and model their inner topology. The hybrid approach we propose can optimize the active nets through the minimization of the model energy functions and, moreover, it can provide some segmentation results unreachable by the GA method alone such as changes in the net topology. [ABSTRACT FROM AUTHOR]

Chromatic number, chromatic sum and chromatic sum number are important graph coloring characteristics. The paper proves that a parallel metaheuristic like the parallel genetic algorithm (PGA) can be efficiently used for computing approximate sum colorings and finding upper bounds for chromatic sums and chromatic sum numbers for hard-to-color graphs. Suboptimal sum coloring with PGA gives usually much closer upper bounds then theoretical formulas known from the literature. [ABSTRACT FROM AUTHOR]

A new Genetic Programming variant called Liquid State Genetic Programming (LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic memory for storing the inputs (the liquid) and a Genetic Programming technique used for the problem solving part. Several numerical experiments with LSGP are performed by using several benchmarking problems. Numerical experiments show that LSGP performs similarly and sometimes even better than standard Genetic Programming for the considered test problems. [ABSTRACT FROM AUTHOR]