The Effects of Constant and Bit-Wise Neutrality on Problem Hardness, Fitness Distance Correlation and Phenotypic Mutation Rates

2013 IEEE Congress on Evolutionary Computation

Cody-Kenny

Trujillo

et al. 2013

Self Cite

Abstract-Research on semantics in Genetic Programming(GP) has increased over the last number of years. Results in this area clearly indicate that its use in GP considerably increases performance. Many of these semantic-based approaches rely on a trial-and-error method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossover-semantics based -CSB). This, in consequence, has a major drawback: these methods could evaluate thousands of nodes, resulting in paying a high computational cost, while attempting to improve performance by promoting semantic diversity. In this work, we propose a simple and computationally inexpensive method, named semantics in selection, that eliminates the computational cost observed in CSB approaches. We tested this approach in 14 GP problems, including continuous-and discrete-valued fitness functions, and compared it against a traditional GP and a CSB approach. Our results are equivalent, and in some cases, superior than those found by the CSB approach, without the necessity of using a "brute force" mechanism.

Section: Introductionmentioning

confidence: 99%

Using semantics in the selection mechanism in Genetic Programming: A simple method for promoting semantic diversity

2013 IEEE Congress on Evolutionary Computation

Cody-Kenny

Trujillo

et al. 2013

Self Cite

“…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

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

Harris

Dusparić

et al. 2012

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.

“…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%

Defining locality as a problem difficulty measure in genetic programming

Genet Program Evolvable Mach

McDermott

O’Neill

et al. 2011

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.