The Effects of Constant Neutrality on Performance and Problem Hardness in GP

Galván, Edgar; Dignum, Stephen; Poli, Riccardo

doi:10.1007/978-3-540-78671-9_27

Cited by 21 publications

(25 citation statements)

References 16 publications

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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%

“…Both [60] and [41] construct examples which demonstrate that the fdc can be "blinded" by particular qualities of the search space, and that it can be misleading. There is, however, a vast amount of work where Jones' approach has been successfully used in a wide variety of problems [12,16,44,45]. Of particular interest is the work by Vanneschi and colleagues [62,65] which concentrated on the use of fdc in the context of GP.…”

Section: Fitness Distance Correlationmentioning

confidence: 99%

“…Of particular interest is the work by Vanneschi and colleagues [62,65] which concentrated on the use of fdc in the context of GP. Recent works using fdc on GP include [11,12]. This is particularly interesting because it has been shown that fdc can be applied using tree-like structures providing a suitable tree distance definition.…”

Section: Fitness Distance Correlationmentioning

confidence: 99%

See 2 more Smart Citations

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

confidence: 92%

Section: Fitness Distance Correlationmentioning

confidence: 99%

Section: Fitness Distance Correlationmentioning

confidence: 99%

See 1 more Smart Citation

Defining locality as a problem difficulty measure in genetic programming

Galván

McDermott

O’Neill

et al. 2011

Genet Program Evolvable Mach

Self Cite

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“…This form of neutrality was first studied using a binary GA [5,3] and then analysed using GP [4]. Briefly, the idea is that in this approach, neutrality is "plugged" into the traditional GP representation by adding a flag to the representation: when the flag is set, the individual is on the neutral network and its fitness has a pre-fixed value (denoted in this work by f n ).…”

Section: Degree Neutralitymentioning

confidence: 99%

An Empirical Investigation of How Degree Neutrality Affects GP Search

Galván

Poli

2009

MICAI 2009: Advances in Artificial Intelligence

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Abstract. Over the last years, neutrality has inspired many researchers in the area of Evolutionary Computation (EC) systems in the hope that it can aid evolution. However, there are contradictory results on the effects of neutrality in evolutionary search. The aim of this paper is to understand how neutralitynamed in this paper degree neutrality -affects GP search. For analysis purposes, we use a well-defined measure of hardness (i.e., fitness distance correlation) as an indicator of difficulty in the absence and in the presence of neutrality, we propose a novel approach to normalise distances between a pair of trees and finally, we use a problem with deceptive features where GP is well-known to have poor performance and see the effects of neutrality in GP search.

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“…Moreover, this is also true for evolutionary algorithms (EAs), where the search for better fitness guides the evolutionary process. Fitness is the core element in understanding EA dynamics, with a large amount of research devoted at describing, understanding and analyzing fitness functions [1] and fitness landscapes [2], [3]; such as work done on locality [4], [5] and neutrality [6], [7], [8]. Fitness-based algorithms have achieved impressive results in many domains, for instance just considering the genetic programming (GP) paradigm a long list of examples exist [9].…”

Section: Introductionmentioning

confidence: 99%

Searching for novel regression functions

Martínez

Naredo

Trujillo

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-The objective function is the core element in most search algorithms that are used to solve engineering and scientific problems, referred to as the fitness function in evolutionary computation. Some researchers have attempted to bridge this difference by reducing the need for an explicit fitness function. A noteworthy example is the novelty search (NS) algorithm, that substitutes fitness with a measure of uniqueness, or novelty, that each individual introduces into the search. NS employs the concept of behavioral space, where each individual is described by a domain-specific descriptor that captures the main features of an individual's performance. However, defining a behavioral descriptor is not trivial, and most works with NS have focused on robotics. This paper is an extension of recent attempts to expand the application domain of NS. In particular, it represents the first attempt to apply NS on symbolic regression with genetic programming (GP). The relationship between the proposed NS algorithm and recent semantics-based GP algorithms is explored. Results are encouraging and consistent with recent findings, where NS achieves below average performance on easy problems, and achieves very good performance on hard problems. In summary, this paper presents the first attempt to apply NS on symbolic regression, a continuation of recent research devoted at extending the domain of competence for behavior-based search.

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The Effects of Constant Neutrality on Performance and Problem Hardness in GP

Cited by 21 publications

References 16 publications

Defining locality as a problem difficulty measure in genetic programming

Defining locality as a problem difficulty measure in genetic programming

An Empirical Investigation of How Degree Neutrality Affects GP Search

Searching for novel regression functions

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