Analysis of speciation and niching in the multi-niche crowding GA

Cedeño, Walter; Vemuri, V. Rao

doi:10.1016/s0304-3975(99)00093-6

Cited by 30 publications

(33 citation statements)

References 6 publications

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“…In this context, each optima can be exploited by a sub-population that can be seen as a sub-species [15] specialized (crowded) on a "niche". One can even promote crossover only inside the subpopulations [15,2] ("intra-niche" crossover), so as to "crowd" new individuals on the same niches. One of the bestknown niching methods is crowding [4,2].…”

Section: Crowding In Multi-modal Optimizationmentioning

confidence: 99%

“…Furthermore, these spacing components can be "attached" to an existing MA without any change on the internal MA operators (SMA re-uses the MA crossover, local search, parent and survival selection, etc.). Besides introducing new techniques in memetic computing, SMA unifies several interesting ideas developed over time in rather disparate contexts (Section 3)-e.g., offspring addition tests in discrete optimization [20,21], niching and crowding in multimodal optimization [15,4,2,19], distanceguided recombination [8,6], and others [23,9,5].…”

Section: Introduction and Motivationsmentioning

confidence: 99%

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Spacing memetic algorithms

Porumbel

Hao

Kuntz

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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We introduce the Spacing Memetic Algorithm (SMA), a formal evolutionary model devoted to a systematic control of spacing (distances) among individuals. SMA uses search space distance information to decide what individuals are acceptable in the population, what individuals need to be replaced and when to apply mutations. By ensuring a "healthy" spacing (and thus diversity), SMA substantially reduces the risk of premature convergence and helps the search process to continuously discover new high-quality search areas. Generally speaking, the number of distance calculations represents a limited computational overhead compared to the number of local search iterations. Most existing memetic algorithms can be "upgraded" to a spacing memetic algorithm, provided that a suitable distance measure can be specified. The impact of the main SMA components is assessed within several case studies on different problems.

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Section: Crowding In Multi-modal Optimizationmentioning

confidence: 99%

Section: Introduction and Motivationsmentioning

confidence: 99%

Spacing memetic algorithms

Porumbel

Hao

Kuntz

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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“…In this context, niching methods (e.g. fitness sharing or crowding [Mahfoud, 1995a;Mahfoud, 1995b;Deb and Goldberg, 1989;Miller and Shaw, 1996;Goldberg and Richardson, 1987;De Jong, 1975;Cedeño and Vemuri, 1999;) were introduced for the "formation and maintenance of stable subpopulations" [Mahfoud, 1995a;Mahfoud, 1995b]. Each subpopulation is devoted to a peak of the multi-modal function and it can be seen as a sub-species [Miller and Shaw, 1996] that exploits a "niche".…”

Section: Other Evolutionary Computation Fieldsmentioning

confidence: 99%

“…For multimodal functions, the niches correspond to the several peaks of the function and one often promotes crossover only inside the subpopulations [Miller and Shaw, 1996;Cedeño and Vemuri, 1999] (intra-niche crossover). In our memetic algorithm, we prefer "inter-niche" crossover because this is the best tool to discover new regions.…”

Section: Other Evolutionary Computation Fieldsmentioning

confidence: 99%

“…de Jong's crowding [De Jong, 1975], Mahfoud's deterministic crowding [Mahfoud, 1995a], and others [Cedeño and Vemuri, 1999]) tries to "'induce niches by forcing new individuals to replace individuals that are similar genomically"' [Smith et al, 1993]. In this manner, a new individual replaces one from its own niche or subpopulation and it minimizes changes on the parent population, i.e.…”

Section: Other Evolutionary Computation Fieldsmentioning

confidence: 99%

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Heuristic algorithms and learning techniques: applications to the graph coloring problem

Porumbel¹

2011

4OR-Q J Oper Res

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A molecular evolution algorithm for ligand design in DOCK

et al. 2022

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As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single‐molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein‐ligand systems. Ensemble‐based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS‐CoV‐2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run‐time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.

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Analysis of speciation and niching in the multi-niche crowding GA

Cited by 30 publications

References 6 publications

Spacing memetic algorithms

Spacing memetic algorithms

Heuristic algorithms and learning techniques: applications to the graph coloring problem

A molecular evolution algorithm for ligand design in DOCK

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