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This paper presents novel approach to applying growth control and diseases mechanisms in computational embryogeny. Our method, which mimics fundamental processes from biology, enables individuals to reach maturity in a controlled process through a stochastic environment. Three different mechanisms were implemented; disease mechanisms, gene suppression, and thermodynamic balancing. This approach was integrated as part of a structural evolutionary model. The model evolved continuum3-D structures which support an external load. By using these mechanisms we were able to evolve individuals that reached a fixed size limit through the growth process. The growth process was an integral part of the complete development process. The size of the individuals was determined purely by the evolutionary process where different individuals matured to different sizes. Individuals which evolved with these characteristics have been found to be very robust for supporting a wide range of external loads.

Modularity and symmetry are two properties observed in almost every engineering and biological structure. The origin of these properties in nature is still unknown. Yet, as engineers we tend to generate designs which share these properties. In this paper we will report on the origin of these properties in three dimensional evolved structures (phenotypes). The phenotypes were evolved in an evolutionarydevelopmental model of biological structures. The phenotypes were grown under a high volatility stochastic environment. The phenotypes have evolved to function within the environment using the very basic requirements. Even though neither modularity nor symmetry have been directly imposed as part of the requirements, the phenotypes were able to generate these properties after only a few hundred generations. These results may suggest that modularity and symmetry are both very fundamental properties that develop during the early stages of evolution. This result may give insight to the origin of both modularity and symmetry in biological organisms.

Facial composite systems are used to create a likeness to a suspect in criminal investigations. Traditional, feature-based facial composite systems rely on the witness' ability to recall individual features, provide verbal descriptions and then select them from stored libraries of labelled features - a task which witnesses often find difficult. The EFIT-V facial composite system is based on different principles, employing a holistic (whole face) approach to construction. The witness is shown a number of randomly generated faces and is asked to select the one that best resembles the target. A genetic algorithm is then used to breed a new generation of faces based upon the selected individual. This process is repeated until the user is satisfied with the composite generated. This paper describes the main components and methodology of EFIT-V and showcases the strengths of the system.

When Genetic Programming is used to evolve arithmetic functions it often operates by composing them from a fixed collection of elementary operators and applying them to parameters or certain primitive constants. This limits the expressiveness of the programs that can be evolved. It is possible to extend the expressiveness of such an approach significantly without leaving the comfort of terminating programs by including primitive recursion as a control operation.The technique used here was gene expression programming [2], a variation of grammatical evolution [8]. Grammatical evolution avoids the problem of program bloat; its separation of genotype (string of symbols) and phenotype (expression tree) permits to optimise the generated programs without interfering with the evolutionary process.

The conventional classification task of data mining can be called single-label classification, since there is a single class attribute to be predicted. This paper addresses a more challenging version of the classification task, where there are two or more class attributes to be predicted. We propose a new ant colony algorithm for the multi-label classification task. The new algorithm, called MuLAM (Multi-Label Ant-Miner) is a major extension of Ant-Miner, the first ant colony algorithm for discovering classification rules. We report results comparing the performance of MuLAM with the performance of three other classification techniques, namely the very simple majority classifier, the original Ant-Miner algorithm and C5.0, a very popular rule induction algorithm. The experiments were performed using five bioinformatics datasets, involving the prediction of several kinds of protein function.

The Ant-Miner algorithm, first proposed by Parpinelli and colleagues, applies an ant colony optimization heuristic to the classification task of data mining to discover an ordered list of classification rules. In this paper we present a new version of the Ant-Miner algorithm, which we call Unordered Rule Set Ant-Miner, that produces an unordered set of classification rules. The proposed version was evaluated against the original Ant-Miner algorithm in six public-domain datasets and was found to produce comparable results in terms of predictive accuracy. However, the proposed version has the advantage of discovering more modular rules, i.e., rules that can be interpreted independently from other rules - unlike the rules in an ordered list, where the interpretation of a rule requires knowledge of the previous rules in the list. Hence, the proposed version facilitates the interpretation of discovered knowledge, an important point in data mining.

This paper proposes a constrained-syntax genetic programming (GP) algorithm-for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for mining the data.

Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism. This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming. Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in program induction.

For a given data set, its set of attributes defines its data space representation. The quality of a data space representation is one of the most important factors influencing the performance of a data mining algorithm. The attributes defining the data space can be inadequate, making it difficult to discover high-quality knowledge. In order to solve this problem, this paper proposes a Genetic Programming algorithm developed for attribute construction. This algorithm constructs new attributes out of the original attributes of the data set, performing an important preprocessing step for the subsequent application of a data mining algorithm.

Genetic Programming is capable of automatically inducing symbolic computer programs on the basis of a set of examples or their performance in a simulation. Mathematical expressions are a well-defined subset of symbolic computer programs and are also suitable for optimization using the genetic programming paradigm. The induction of mathematical expressions based on data is called symbolic regression. In this work, genetic programming is extended to not just fit the data i.e., get the numbers right, but also to get the dimensions right. For this units of measurement are used. The main contribution in this work can be summarized as: The symbolic expressions produced by genetic programming can be made suitable for analysis and interpretation by using units of measurements to guide or restrict the search. To achieve this, the following has been accomplished: A standard genetic programming system is modified to be able to induce expressions that more-or-less abide type constraints. This system is used to implement a preferential bias towards dimensionally correct solutions. A novel genetic programming system is introduced that is able to induce expressions in languages that need context-sensitive constraints. It is demonstrated that this system can be used to implement a declarative bias towards 1) the exclusion of certain syntactical constructs; 2) the induction of expressions that use units of measurement; 3) the induction of expressions that use matrix algebra; 4) the induction of expressions that are numerically stable and correct. A case study using four real-world problems in the induction of dimensionally correct empirical equations on data using the two different methods is presented to illustrate to use and limitations of these methods in a framework of scientific discovery.

Genetic Programming is capable of automatically inducing symbolic computer programs on the basis of a set of examples or their performance in a simulation. Mathematical expressions are a well-defined subset of symbolic computer programs and are also suitable for optimization using the genetic programming paradigm. The induction of mathematical expressions based on data is called symbolic regression. In this work, genetic programming is extended to not just fit the data i.e., get the numbers right, but also to get the dimensions right. For this units of measurement are used. The main contribution in this work can be summarized as: The symbolic expressions produced by genetic programming can be made suitable for analysis and interpretation by using units of measurements to guide or restrict the search. To achieve this, the following has been accomplished: A standard genetic programming system is modified to be able to induce expressions that more-or-less abide type constraints. This system is used to implement a preferential bias towards dimensionally correct solutions. A novel genetic programming system is introduced that is able to induce expressions in languages that need context-sensitive constraints. It is demonstrated that this system can be used to implement a declarative bias towards 1) the exclusion of certain syntactical constructs; 2) the induction of expressions that use units of measurement; 3) the induction of expressions that use matrix algebra; 4) the induction of expressions that are numerically stable and correct. A case study using four real-world problems in the induction of dimensionally correct empirical equations on data using the two different methods is presented to illustrate to use and limitations of these methods in a framework of scientific discovery.

Genetic Programming is capable of automatically inducing symbolic computer programs on the basis of a set of examples or their performance in a simulation. Mathematical expressions are a well-defined subset of symbolic computer programs and are also suitable for optimization using the genetic programming paradigm. The induction of mathematical expressions based on data is called symbolic regression. In this work, genetic programming is extended to not just fit the data i.e., get the numbers right, but also to get the dimensions right. For this units of measurement are used. The main contribution in this work can be summarized as: The symbolic expressions produced by genetic programming can be made suitable for analysis and interpretation by using units of measurements to guide or restrict the search. To achieve this, the following has been accomplished: A standard genetic programming system is modified to be able to induce expressions that more-or-less abide type constraints. This system is used to implement a preferential bias towards dimensionally correct solutions. A novel genetic programming system is introduced that is able to induce expressions in languages that need context-sensitive constraints. It is demonstrated that this system can be used to implement a declarative bias towards 1) the exclusion of certain syntactical constructs; 2) the induction of expressions that use units of measurement; 3) the induction of expressions that use matrix algebra; 4) the induction of expressions that are numerically stable and correct. A case study using four real-world problems in the induction of dimensionally correct empirical equations on data using the two different methods is presented to illustrate to use and limitations of these methods in a framework of scientific discovery.