Neutrality in evolutionary algorithms… What do we know?

Galván, Edgar; Poli, Riccardo; Kattan, Ahmed; O’Neill, Michael; Brabazon, Anthony

doi:10.1007/s12530-011-9030-5

Cited by 45 publications

(31 citation statements)

References 84 publications

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“…For example, we are considering the use of more devices (agents), we are also planning in using other forms on CI. In particular we are interested in using Evolutionary Algorithms and their novel research on problem hardness (e.g., locality [3], [4], [5], neutrality [2], [6], [11]) to, for example, speed the learning process up.…”

Section: Discussionmentioning

confidence: 99%

Reducing electricity costs in a dynamic pricing environment

Galván

Harris

Dusparić

et al. 2012

2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-Smart Grid technologies are becoming increasingly dynamic, so the use of computational intelligence is becoming more and more common to support the grid to automatically and intelligently respond to certain requests (e.g., reducing electricity costs giving a pricing history). In this work, we propose the use of a particular computational intelligence approach, denominated Distributed W-Learning, that aims to reduce electricity costs in a dynamic environment (e.g., changing prices over a period of time) by turning electric devices on (i.e., clothes dryer, electric vehicle) at residential level, at times when the electricity price is the lowest, while also, balancing the use of energy by avoiding turning on the devices at the same time. We make this problem as realistic as possible, by considering the use of real-world constraints (e.g., time to complete a task, boundary times within which a device can be used). Our results clearly indicate that the use of computational intelligence can be beneficial in this type of dynamic and complex problems.

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Section: Discussionmentioning

confidence: 99%

Reducing electricity costs in a dynamic pricing environment

Galván

Harris

Dusparić

et al. 2012

2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)

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“…As argued in [16], much of this contention stems from the overly complex problems, representations, and search algorithms used in these investigations, which make it difficult to tease apart the effects of neutrality from other confounding factors. In addition, neutrality is often artificially added to the problem representation and little attention is paid to how this alters the fitness landscape.…”

Section: Discussionmentioning

confidence: 99%

“…The utility of neutrality in GP is a contentious topic [16]. While some studies have found no benefit [7,44,45], others have claimed that neutrality buffers against deleterious genetic perturbation [20,46,57] and reduces the risk of premature convergence through an expansion of the search space [12,18].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming

Payne

Banzhaf

et al. 2012

Genet Program Evolvable Mach

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Redundancy is a ubiquitous feature of genetic programming (GP), with many-to-one mappings commonly observed between genotype and phenotype, and between phenotype and fitness. If a representation is redundant, then neutral mutations are possible. A mutation is phenotypically-neutral if its application to a genotype does not lead to a change in phenotype. A mutation is fitness-neutral if its application to a genotype does not lead to a change in fitness. Whether such neutrality has any benefit for GP remains a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, such as success rate or search efficiency, to investigate the utility of neutrality in GP. Here, we take a different tack and use a measure of robustness to quantify the neutrality associated with each genotype, phenotype, and fitness value. We argue that understanding the influence of neutrality on GP requires an understanding of the distributions of robustness at these three levels, and of the interplay between robustness, evolvability, and accessibility amongst genotypes, phenotypes, and fitness values. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration and allows for the full characterization of these quantities, which we then relate to the Ting Hu and Joshua L. Payne contributed equally to this work. dynamical properties of simple mutation-based evolutionary processes. Our results demonstrate that it is not only the distribution of robustness amongst phenotypes that affects evolutionary search, but also (1) the distributions of robustness at the genotypic and fitness levels and (2) the mutational biases that exist amongst genotypes, phenotypes, and fitness values. Of crucial importance is the relationship between the robustness of a genotype and its mutational bias toward other phenotypes.

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“…In other words synonymous redundancy (redundancy of genotypic neighbours) has the same effect as a small divergence of genotypic neighbours. Whether neutrality is beneficial in general is a complex question considered in detail in some previous work [11,12,16,19,37,44,45,61]; this issue deserves consideration as we will see. The situation is summarised (for discrete-valued phenotypic distances) in Table 1.…”

Section: Extending the Definition Of Locality To The Genotype-fitnessmentioning

confidence: 92%

Defining locality as a problem difficulty measure in genetic programming

Galván

McDermott

O’Neill

et al. 2011

Genet Program Evolvable Mach

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Publication information Genetic Programming and Evolvable Machines, 12 (4): 365-401Publisher Springer Abstract A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bitstring-based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the genotype and the phenotype are not distinct. We attempt to extend the standard quantitative definition of genotype-phenotype locality to the genotype-fitness mapping by considering three possible definitions. We consider the effects of these definitions in both continuous-and discrete-valued fitness functions. We compare three different GP representations (two of them induced by using different function sets and the other using a slightly different GP encoding) and six different mutation operators. Results indicate that one definition of locality is better in predicting performance.

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Neutrality in evolutionary algorithms… What do we know?

Cited by 45 publications

References 84 publications

Reducing electricity costs in a dynamic pricing environment

Reducing electricity costs in a dynamic pricing environment

Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming

Defining locality as a problem difficulty measure in genetic programming

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