Genetic algorithm with search area adaptation for the function optimization and its experimental analysis

Someya, Hiroshi; Yamamura, Masatoshi

doi:10.1109/cec.2001.934290

Cited by 15 publications

(8 citation statements)

References 12 publications

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“…67). Furthermore, the taxonomy may be extended with the aim of classifying crossover operators that are applicable to more than two parents: UNDX, 68 BNDX, 69 TMX, 70 UNDX-m, 37 SPX, 39 EDX, 71 PCX 34 and crossover based on confidence intervals. 38 Finally, another open issue concerns the use of different aggregation functions or probability distributions for generating the genes of an offspring.…”

Section: Discussionmentioning

confidence: 99%

A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

Herrera

Lozano

Sánchez

2003

Int. J. Intell. Syst.

339

184

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The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models.

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Section: Discussionmentioning

confidence: 99%

A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

Herrera

Lozano

Sánchez

2003

Int. J. Intell. Syst.

339

184

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“…Trimodal distribution crossover (TMX) has been proposed by the authors in [22] for applying GSA to the function optimization. TMX is designed with inheritance ratio of children in mind.…”

Section: Tmx [22]mentioning

confidence: 99%

A robust real-coded evolutionary algorithm with toroidal search space conversion

Someya

Yamamura

2004

Soft Comput

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This paper presents a robust Real-coded evolutionary algorithm. Real-coded evolutionary algorithms (RCEAs), such as real-coded genetic algorithms and evolution strategies, are known as effective methods for function optimization. However, they often fail to find the optimum in case the objective function is multimodal, discrete or high-dimensional. It is also reported that most crossover (or recombination) operators for RCEAs has sampling bias that prevents to find the optimum near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of search space. In this paper, we apply two methods, genetic algorithm with search area adaptation (GSA) and toroidal search space conversion (TSC), to the function optimization for improving the robustness of RCEAs. The former method searches adaptively and the latter one removes the sampling bias. Through several experiments, we have confirmed that GSA works adaptively and it shows higher performance, and RCEAs with TSC show effectiveness to find the optima near the boundary of search space.

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“…30,51,53,59,60,61 • Some forms of crossover operators are more suitable to tackle certain problems than others. For this reason, techniques that combine multiple crossovers have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population.…”

Section: Discussionmentioning

confidence: 99%

Real-parameter crossover operators with multiple descendents: An experimental study

Sánchez

Lozano

García-Martínez

et al. 2008

Int. J. Intell. Syst.

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Crossover operators with multiple descendents produce more than two offspring for each pair of parents. They were suggested as an alternative method to the common practice of generating only two offspring per couple. An offspring selection mechanism is responsible for choosing the two offspring that become the children contributed by the mating. Recently, there has been an increasing interest in incorporating this crossover scheme into real-coded genetic algorithm models because its operation was particularly suitable to attain reliable and accurate solutions for many continuous optimization problems.In this paper, we undertake an extensive empirical study of the main factors that affect the performance of real-parameter crossover operator with multiple descendents. To do this, we focus our attention on three well-known neighborhood-based real-parameter crossover operators, BLX-α, fuzzy recombination, and PNX. The experimental results obtained confirm that the generation of multiple descendents along with the offspring selection mechanism that chooses the two best offspring may enhance the operation of these three crossover operators. Another important finding from our experiments is that real-coded genetic algorithms with crossover operators with multiple descendents are more efficient than standard real-coded genetic algorithms, that is, they offer solutions with higher quality, requiring fewer fitness function evaluations. C 2008 Wiley Periodicals, Inc.

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Genetic algorithm with search area adaptation for the function optimization and its experimental analysis

Cited by 15 publications

References 12 publications

A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

A robust real-coded evolutionary algorithm with toroidal search space conversion

Real-parameter crossover operators with multiple descendents: An experimental study

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