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23
Redundant Representation in Evolutionary Computation.
- Evolutionary Computation,
, 2003
"... Abstract This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redund ..."
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Abstract This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2 kr /r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.
Development brings scalability to hardware evolution
- In Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware (Washington DC, U.S.A, July 2005), IEEE Computer Society
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Coevolution of intelligent agents using cartesian genetic programming
- GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
, 2007
"... A coevolutionary competitive learning environment for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based on a compartmentalised model of neurons. The genetic basis of neurons is an important [27] and neglected aspect of previous approaches. A ..."
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Cited by 16 (15 self)
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A coevolutionary competitive learning environment for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based on a compartmentalised model of neurons. The genetic basis of neurons is an important [27] and neglected aspect of previous approaches. Accordingly, we have defined a collection of chromosomes representing various aspects of the neuron: soma, dendrites and axon branches, and synaptic connections. Chromosomes are represented and evolved using a form of genetic programming (GP) known as Cartesian GP. The network formed by running the chromosomal 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 environmental interactions. The idea of this paper is to demonstrate the importance of the genetic transfer of learned experience and life time learning. The learning is a consequence of the complex dynamics produced as a result of interaction (coevolution) between two intelligent agents. Our results show that both agents exhibit interesting learning capabilities. Categories and Subject Descriptors
The Root Causes of Code Growth in Genetic Programming
- In
, 2003
"... This paper discusses the underlying pressures responsible for code growth in genetic programming, and shows how an understanding of these pressures can be used to use to eliminate code growth while simultaneously improving performance. We begin with a discussion of two distinct components of cod ..."
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Cited by 10 (0 self)
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This paper discusses the underlying pressures responsible for code growth in genetic programming, and shows how an understanding of these pressures can be used to use to eliminate code growth while simultaneously improving performance. We begin with a discussion of two distinct components of code growth and the extent to which each component is relevant in practice. We then define the concept of resilience in GP trees, and show that the buildup of resilience is essential for code growth. We present simple modifications to the selection procedures used by GP that eliminate bloat without hurting performance. Finally, we show that eliminating bloat can improve the performance of genetic programming by a factor that increases as the problem is scaled in difficulty.
An Evolutionary System using Development and Artificial Genetic Regulatory Networks
- Proceedings of 9th IEEE World Congress on Computational Intelligence. Congress on Evolutionary Computation, Special Session on Evolving Gene Regulatory Networks, IEEE
, 2008
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
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Cited by 10 (3 self)
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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
Functional Genetic Programming with Combinators
"... Abstract. Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation ..."
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Cited by 5 (0 self)
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Abstract. Prior program representations for genetic programming that incorporated features of modern programming languages solved harder problems than earlier representations, but required more complex genetic operators. We develop the idea of using combinator expressions as a program representation for genetic programming. This representation makes it possible to evolve programs with a variety of programming language constructs using simple genetic operators. We investigate the effort required to evolve combinator-expression solutions to several problems: linear regression, even parity on N inputs, and implementation of the stack and queue data structures. Genetic programming with combinator expressions compares favorably to prior approaches, namely the works
Deceptiveness and Neutrality The ND Family of Fitness Landscapes
"... When a considerable number of mutations have no effects on fitness values, the fitness landscape is said neutral. In order to study the interplay between neutrality, which exists in many real-world applications, and performances of metaheuristics, it is useful to design landscapes which make it poss ..."
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When a considerable number of mutations have no effects on fitness values, the fitness landscape is said neutral. In order to study the interplay between neutrality, which exists in many real-world applications, and performances of metaheuristics, it is useful to design landscapes which make it possible to tune precisely neutral degree distribution. Even though many neutral landscape models have already been designed, none of them are general enough to create landscapes with specific neutral degree distributions. We propose three steps to design such landscapes: first using an algorithm we construct a landscape whose distribution roughly fits the target one, then we use a simulated annealing heuristic to bring closer the two distributions and finally we affect fitness values to each neutral network. Then using this new family of fitness landscapes we are able to highlight the interplay between deceptiveness and neutrality.
The Effects of Constant and Bit-Wise Neutrality on Problem Hardness, Fitness Distance Correlation and Phenotypic Mutation Rates
"... Kimura’s neutral theory of evolution has inspired researchers from the evolutionary computation community to incorporate neutrality into Evolutionary Algorithms (EAs) in the hope that it can aid evolution. The effects of neutrality on evolutionary search have been considered in a number of studies, ..."
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Cited by 4 (0 self)
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Kimura’s neutral theory of evolution has inspired researchers from the evolutionary computation community to incorporate neutrality into Evolutionary Algorithms (EAs) in the hope that it can aid evolution. The effects of neutrality on evolutionary search have been considered in a number of studies, the results of which, however, have been highly contradictory. In this paper, we analyse the reasons for this and we make an effort to shed some light on neutrality by addressing them. We consider two very simple forms of neutrality: constant neutrality — a neutral network of constant fitness, identically distributed in the whole search space — and bit-wise neutrality, where each phenotypic bit is obtained by transforming a group of genotypic bits via an encoding function. We study these forms of neutrality both theoretically and empirically (both for standard benchmark functions and a class of random MAX-SAT problems) to see how and why they influence the behaviour and performance of a mutation-based EA. In particular, we analyse how the fitness distance correlation of landscapes changes under the effect of different neutral encodings and how phenotypic mutation rates vary as a function of genotypic mutation rates. Both help explain why the behaviour of a mutation-based EA may change so radically as problem, form of neutrality and mutation rate are varied.
Neutrality and gradualism: encouraging exploration and exploitation simultaneously with Binary Decision Diagrams
- In (to appear in) Proceedings of the 2006 IEEE Congress on Evolutionary Computation
, 2006
"... Abstract — Search algorithms are subject to the trappings of local optima. Attempts to address the problem are often framed in the context of needing to balance, or trade-off, exploitation against exploration. Ideally, it is best to maximise both simultaneously, but this is usually seen as infeasibl ..."
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Cited by 2 (2 self)
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Abstract — Search algorithms are subject to the trappings of local optima. Attempts to address the problem are often framed in the context of needing to balance, or trade-off, exploitation against exploration. Ideally, it is best to maximise both simultaneously, but this is usually seen as infeasible in the presence of multi-modal search spaces. This paper investigates the potential for exploration of both neutrality and mutation rate, and argues that the former is the more important. The most interesting result, however, is that the necessity for a trade-off between exploitation and exploration can be avoided within the context of our algorithm for evolving Binary Decision Diagrams. I.
A quantitative study of neutrality in GP boolean landscapes
- Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’06
, 2006
"... Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disrega ..."
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Cited by 2 (1 self)
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Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disregarded by uniform random sampling, and we introduce new genetic operators to define the neighborhood of tree structures. We compare the fitness landscape induced by two different sets of functional operators ({nand} and {xor; not}). The different characteristics of the neutral networks seem to justify the different difficulties of these landscapes for genetic programming.
