Search

Self-Modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. In addition to the usual computational functions found in CGP, SMCGP includes functions that can modify the evolved program at run time. This means that programs can be iterated to produce an infinite sequence of phenotypes from a single evolved genotype. Here, we discuss the results of using SMCGP on a variety of different problems, and see that SMCGP is able to solve tasks that require scalability and plasticity. We demonstrate how SMCGP is able to produce results that would be impossible for conventional, static Genetic Programming techniques. [ABSTRACT FROM AUTHOR]

Cartesian Genetic Programming (CGP) is applied to solving differential equations (DE). We illustrate that repeated elements in analytic solutions to DE can be exploited under GP. An analysis is carried out of the search space in tree and CGP frameworks, examining the complexity of different DE problems. Experimental results are provided against benchmark ordinary and partial differential equations. A system of ordinary differential equations (SODE) is solved using multiple outputs from a genome. We discuss best heuristics when generating DE solutions through evolutionary search. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms can be used to evolve novel digital circuit solutions. This paper proposes the use of flexible target truth tables, allowing evolution more freedom where values are undefined. This concept is applied to three test circuits with different distributions of ˵don΄t care″ values. Two strategies are introduced for utilising the undefined output values within the evolutionary algorithm. The use of flexible desired truth tables is shown to significantly improve the success of the algorithm in evolving circuits to perform this function. In addition, we show that this flexibility allows evolution to develop more hardware efficient solutions than using a fully-defined truth table. [ABSTRACT FROM AUTHOR]

This paper explores the application of an artificial developmental system (ADS) to the field of evolutionary robotics by investigating the capability of a gene regulatory network (GRN) to specify a general purpose obstacle avoidance controller both in simulation and on a real robot. Experiments are carried out using the e-puck robot platform. It is further proposed to use cross-correlation between inputs and outputs in order to assess the quality of robot controllers more accurately than with observing its behaviour alone. [ABSTRACT FROM AUTHOR]

Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It is able to modify its own phenotype during execution of the evolved program. This is done by the inclusion of modification operators in the function set. Here we present the use of the technique on several different sequence generation and regression problems. [ABSTRACT FROM AUTHOR]

This paper presents a novel representation of Cartesian genetic programming (CGP) in which multiple networks are used in the classification of high resolution X-rays of the breast, known as mammograms. CGP networks are used in a number of different recombination strategies and results are presented for mammograms taken from the Lawrence Livermore National Laboratory database. [ABSTRACT FROM AUTHOR]

The majority of artificial neural networks are static and lifeless and do not change themselves within a learning environment. In these models learning is seen as the process of obtaining the strengths of connections between neurons (i.e. weights). We refer to this as the ΄synaptic dogma΄. This is in marked contrast with biological networks which have time dependent morphology and in which practically all neural aspects can change or be shaped by mutual interactions and interactions with an external environment. Inspired by this and many aspects of neuroscience, we have designed a new kind of neural network. In this model, neurons are represented by seven evolved programs that model particular components and aspects of biological neurons (dendrites, soma, axons, synapses, electrical and developmental behaviour). Each network begins as a small randomly generated network of neurons. When the seven programs are run, the neurons, dendrites, axons and synapses can increase or decrease in number and change in interaction with an external environment. Our aim is to show that it is possible to evolve programs that allow a network to learn through experience (i.e. encode the ability to learn). We report on our continuing investigations in the context of learning how to play checkers. [ABSTRACT FROM AUTHOR]

We present a method for constructing electronic circuits that uses analogues of biological multi-cellular development, genetic regulatory networks, and transcription and translation processes to build circuits. We show how small circuits may be evolved and how they may be reused to build larger circuits. We also demonstrate that the artificial `organisms΄ are capable of regeneration so that circuit functionality can be recovered after damage. [ABSTRACT FROM AUTHOR]

This paper presents a method for co-evolving neuro-inspired developmental programs for playing checkers. Each player΄s program is represented by seven chromosomes encoding digital circuits, using a form of genetic programming, called Cartesian Genetic Programming (CGP). The neural network that occurs by running the genetic programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change in response to situations encountered on the checkers board. The results show that, after a number of generations, by playing each other the agents play much better than those from earlier generations. Such learning abilities are encoded at a genetic level rather than at the phenotype level of neural connections. [ABSTRACT FROM AUTHOR]

Evolution is particularly good at finding specific solutions, which are only valid for exactly the input and environment that are presented during evolution. In most evolution experiments the input pattern order problem is not considered, even though the ability to provide a correct result for any input pattern is a prerequisite for valid circuits. Therefore, the importance of including randomness in the input pattern applied during evolution is addressed in this paper. This is shown to be mandatory–particularly in the case of unconstrained intrinsic evolution of digital circuits–in order to find valid solutions. The different ways in which unconstrained evolution and constrained evolution exploit resources of a hardware substrate are compared. It is also shown that evolution benefits from versatile input configurations. Furthermore, hierarchical fitness functions, previously introduced to improve the evolution of combinatorial circuits, are applied to the evolution of sequential circuits. [ABSTRACT FROM AUTHOR]

In our previous work, we have demonstrated that evolution can be used to program liquid crystal to act as a signal processing device. In this work we discuss the stability and reconfigurability of a real time robot controller evolved in liquid crystal. We envisage these issues will be important when programming or evolving in other physical systems. [ABSTRACT FROM AUTHOR]

A biologically inspired developmental model targeted at hardware implementation (off-shelf FPGA) is proposed which exhibits extremely robust transient fault-tolerant capability. All cells in this model have identical genotype (physical structures), and only differ in internal states. In a 3x3 cell digital organism, some individuals which implement a 2-bit multiplier were discovered using evolution that have the ability to "recover" themselves from almost any kinds of transient faults. An intrinsic evolvable hardware platform based on FPGA was realized to speed up the evolution process. [ABSTRACT FROM AUTHOR]

Cartesian Genetic Programming is a graph based representation that has many benefits over traditional tree based methods, including bloat free evolution and faster evolution through neutral search. Here, an integer based version of the representation is applied to a traditional problem in the field: evolving an obstacle avoiding robot controller. The technique is used to rapidly evolve controllers that work in a complex environment and with a challenging robot design. The generalisation of the robot controllers in different environments is also demonstrated. A novel fitness function based on chemical gradients is presented as a means of improving evolvability in such tasks. [ABSTRACT FROM AUTHOR]

A review is given of approaches to growing neural networks and electronic circuits. A new method for growing graphs and circuits using a developmental process is discussed. The method is inspired by the view that the cell is the basic unit of biology. Programs that construct circuits are evolved to build a sequence of digital circuits at user specified iterations. The programs can be run for an arbitrary number of iterations so circuits of huge size could be created that could not be evolved. It is shown that the circuit building programs are capable of correctly predicting the next circuit in a sequence of larger even parity functions. The new method however finds building specific circuits more difficult than a non-developmental method. [ABSTRACT FROM AUTHOR]

In this paper a gate-level evolvable hardware technique for designing multiple-valued (MV) combinational circuits is proposed for the first time. In comparison with the decomposition techniques used for synthesis of combinational circuits previously employed, this new approach is easily adapted for the different types of MV gates associated with operations corresponding to different algebra types and can include other more complex logical expressions (e.g. singlecontrol MV multiplexer called T-gate). The technique is based on evolving the functionality and connectivity of a rectangular array of logic cells. The experimental results show how the success of genetic algorithm depends on the number of columns, the number of rows in circuit structure and levels-back parameter (the number of columns to the left of current cell to which cell input may be connected). We show that the choice of the set of MV gates used radically affects the chances of successful evolution (in terms of number of 100% functional solutions found). [ABSTRACT FROM AUTHOR]

In this paper we present a new chromosome representation for evolving digital circuits. The representation is based very closely on the chip architecture of the Xilinx 6216 FPGA. We examine the effectiveness of evolving circuit functionality by using randomly chosen examples taken from the truth table. We consider the merits of a cell architecture in which functional cells alternate with routing cells and compare this with an architecture in which any cell can implement a function or be merely used for routing signals. It is noteworthy that the presence of elitism significantly improves the Genetic Algorithm performance. [ABSTRACT FROM AUTHOR]

The traditional paradigm for digital filter design is based on the concept of a linear difference equation with the output response being a weighted sum of signal samples with usually floating point coefficients. Unfortunately such a model is necessarily expensive in terms of hardware as it requires many large bit additions and multiplications. In this paper it is shown how it is possible to evolve a small rectangular array of logic gates to perform low pass FIR filtering. The circuit is evolved by assessing its response to digitised pure sine waves. The evolved circuit is demonstrated to possess nearly linear properties, which means that it is capable of filtering composite signals which it has never seen before. [ABSTRACT FROM AUTHOR]

This paper describes experiments to determine how the architecture vis-a-vis routing and functional resources affect the ease with which combinational logic designs may be evolved on a field-programmable gate array (FPGA). We compare two chromosome representations with differing levels of connectivity, and show that the amount of routing and functional resource have a marked effect on the success of the evolutionary process. [ABSTRACT FROM AUTHOR]

We study the evolution of computation performed by non-uniform cellular automata in which global information processing appears at two different levels of self-organisation. In our model, the first level of self-organisation is characterised by interactions among cellular macrostructures or computational demes which compete for room in a finite grid of cells. This level is related to the formation, evolution and extinction of macrostructures, and it is designed in a completely local manner. The second level of self-organisation refers to the interactions among the cells within the demes. The model, derived from the cellular programming approach, allows global computation to occur as a result of many local interactions among computational demes of interacting cells. The study reveals some of the mechanisms by which co-evolving demes of non-uniform cellular automata perform non-trivial computation, such as the synchronisation tasks. [ABSTRACT FROM AUTHOR]