A Simple Cellular Genetic Algorithm for Continuous Optimization

Dorronsoro, Bernabè; Alba, Enrique

doi:10.1109/cec.2006.1688665

Cited by 20 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5d), in line with observations made in RNA systems and supporting the intuitive notion that phenotypes formed by many genotypes are easier to access than phenotypes formed by few genotypes. When deleterious mutations were not allowed, as is the case in ''replace if better or equal'' selection strategies [9,24], the relationship between phenotypic robustness and phenotypic evolvability was lost, as measured using either E 1 p or E 2 p . Since phenotypes of lower fitness could only mutate into phenotypes of equal or higher fitness, the connectivity of a phenotype in the phenotype network was arbitrarily determined and there was consequently no relationship between these evolvability measures and phenotypic robustness.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In the third case, phenotypes are assigned fitness values, but deleterious mutations are not allowed. This corresponds to a ''replace if better or equal'' selection strategy [9,24]. The phenotype network is therefore modified P t p ¼ ðU p ; !…”

Section: Genotype Phenotype and Fitness Networkmentioning

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

View full text Add to dashboard Cite

show abstract

“…Since the genetic framework we used, the same as that of [78], is old-fashioned and uses simple genetic operators except for the new crossover, we expect that if the proposed crossover is combined with good operators such as self-adaptive mutation of CMA-ES [26,34], it will be much more improved. Moreover, since the proposed idea is to remove the inherent bias of crossover operators in a given bounded domain, we expect that it can also be applied to other complicated crossover operators in the state-of-the-art real-coded GAs, such as G3 with PCX [16], StGA (Stochastic GA) [82], Cellular GA [20], GA with orthogonal crossover [51], and so on. However, we leave such hybridization to future work, since such improvement is beyond the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation

Yoon

Kim

Moraglio

et al. 2012

Information Sciences

View full text Add to dashboard Cite

We present a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators. This is done by transforming the topology of the hyper-rectangular real space by gluing opposite boundaries and designing a boundary extension method for making the fitness function smooth at the glued boundary. We show the advantages of the proposed crossover by comparing its performance with those of existing ones on test functions that are commonly used in the literature, and a nonlinear regression on a real-world dataset.

show abstract

“…A processor evaluates its individual then performs crossover or mutation with thes neighbors [6], [7], [8]. This approach is well suited for homogeneous and tighly coupled (low latency) processors, such as a supercomputer, however it has also been shown to be effective in peer-topeer environments [9].…”

Section: A Distributed Genetic Searchmentioning

confidence: 99%

Robust Asynchronous Optimization for Volunteer Computing Grids

Desell

Magdon‐Ismail

Szymanski

et al. 2009

2009 Fifth IEEE International Conference on E-Science

View full text Add to dashboard Cite

Abstract-Volunteer computing grids offer significant computing power at relatively low cost to researchers, while at the same time generating public interest in different scientific projects. However, in order to be used effectively, their heterogeneity, volatility and restrictive computing models must be overcome. As these computing grids are open, incorrect or malicious results must also be handled. This paper examines extending the BOINC volunteer computing framework to allow for asynchronous global optimization as applied to scientific computing problems. The asynchronous optimization method used is resilient to faults and the heterogeneous nature of volunteer computing grids, while allowing scalability to tens of thousands of hosts. A work verification strategy that does not require the validation of every result is presented. This is shown to be able to effectively reduce the need for verification done to less than 30% of the reported results, without degrading the performance of the asynchronous search methods. An asynchronous version of particle swarm optimization (APSO) is presented and compared to previously used asynchronous genetic search (AGS) using the MilkyWay@Home BOINC computing project. Both search methods are shown to scale to MilkyWay@Home's current user base, over 75,000 heterogeneous and volatile hosts, something not possible for traditional optimization methods. APSO is shown to provide faster convergence to optimal results while being less sensitive to its search parameters. The verification strategy presented is shown to be effective for both AGS and APSO.

show abstract

A Simple Cellular Genetic Algorithm for Continuous Optimization

Cited by 20 publications

References 11 publications

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

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

A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation

Robust Asynchronous Optimization for Volunteer Computing Grids

Contact Info

Product

Resources

About