Cellular evolutionary algorithm

A cellular evolutionary algorithm (cEA) is a kind of evolutionary algorithm (EA) in which individuals cannot mate arbitrarily, but every one interacts with its closer neighbors on which a basic EA is applied (selection, variation, replacement).

File:evolution of several cEAs.png

The cellular model simulates natural evolution from the point of view of

the individual, which encodes a tentative optimization, learning, or search problem solution. The essential idea of this model is to provide the EA population

with a special structure defined as a connected graph, in which each vertex is an individual who communicates with his

nearest neighbors. Particularly, individuals are conceptually set in a toroidal

mesh, and are only allowed to recombine with close individuals. This leads

to a kind of locality known as "isolation by distance". The set of potential mates

of an individual is called its "neighborhood". It is known that, in this kind

of algorithm, similar individuals tend to cluster creating niches, and these groups

operate as if they were separate sub-populations (islands). There is no

clear borderline between adjacent groups, and close niches could be easily

colonized by competitive niches and potentially merge solution contents during the process. Simultaneously, farther niches can be affected more slowly.

Introduction

A cellular evolutionary algorithm (cEA) usually evolves a structured bidimensional

grid of individuals, although other topologies are also possible. In this grid, clusters of similar individuals are naturally created during evolution, promoting exploration in their boundaries, while exploitation is mainly performed by direct competition and merging inside them.

File:cEA neighborhood types.png

The grid is usually 2D toroidal structure, although

the number of dimensions can be easily extended (to 3D) or reduced (to 1D, e.g. a ring).

The neighborhood of a particular point of the grid (where an individual is

placed) is defined in terms of the Manhattan distance from it to others in the population. Each point of the grid has a neighborhood that overlaps the neighborhoods of nearby individuals. In the basic algorithm, all the neighborhoods have the same size and identical shapes. The two

most commonly used neighborhoods are L5, also called the

Von Neumann or NEWS (North, East, West and South) neighborhood, and C9, also known as the Moore neighborhood. Here, L stands for "linear" while C stands for "compact".

In cEAs, the individuals can only interact with their neighbors in the reproductive

cycle where the variation operators are applied. This reproductive

cycle is executed inside the neighborhood of each individual and, generally,

consists in selecting two parents among its neighbors according to a certain

criterion, applying the variation operators to them (recombination and mutation

for example), and replacing the considered individual by the recently

created offspring following a given criterion, for instance, replace if the offspring

represents a better solution than the considered individual.

Synchronous versus asynchronous

In a regular synchronous cEA, the algorithm proceeds from the very first top left individual to the right and then to the several rows by using the information in the population to create a new temporary population. After finishing with the bottom-right last individual the temporary population is full with the newly computed individuals, and the replacement step starts. In it, the old population is completely and synchronously replaced with the newly computed one according to some criterion. Usually, the replacement keeps the best individual in the same position of both populations, that is, elitism is used.

According to the update policy of the population used, an asynchronous cEA may also be defined and is a well-known issue in cellular automata. In asynchronous cEAs the order in which the individuals in the grid are update changes depending on the choice of criterion: line sweep, fixed random sweep, new random sweep, and uniform choice. All four proceed using the newly computed individual (or the original if better) for the computations of its neighbors.

File:ratio concept in cEAs.png

The overlap of the neighborhoods provides an implicit mechanism of solution migration

to the cEA. Since the best solutions spread smoothly through the

whole population, genetic diversity in the population is preserved longer than

in non structured EAs. This soft dispersion of the best solutions through the

population is one of the main issues of the good tradeoff between exploration

and exploitation that cEAs perform during the search. This tradeoff can be tuned (and by extension the genetic diversity level along

the evolution) by modifying (for instance) the size of the neighborhood used, as

the overlap degree between the neighborhoods grows according to the size of

the neighborhood.

A cEA can be seen as a cellular automaton (CA) with probabilistic

rewritable rules, where the alphabet of the CA is equivalent to the potential

number of solutions of the problem. Hence, knowledge from research in CAs can be applied to cEAs.

Parallelism

Cellular EAs are very amenable to parallelism, thus usually found in the literature of parallel metaheuristics. In particular, fine grain parallelism can be used to assign independent threads of execution to every individual, thus allowing the whole cEA to run on a concurrent or actually parallel hardware platform. In this way, large time reductions can be obtained when running cEAs on FPGAs or GPUs.

However, it is important to stress that cEAs are a model of search, in many senses different from traditional EAs. Also, they can be run in sequential and parallel platforms, reinforcing the fact that the model and the implementation are two different concepts.

See [https://www.springer.com/business/operations+research/book/978-0-387-77609-5 here] for a complete description on the fundamentals for the understanding, design, and application of cEAs.

References

[https://www.springer.com/business/operations+research/book/978-0-387-77609-5 E. Alba, B. Dorronsoro, Cellular Genetic Algorithms], Springer-Verlag, {{ISBN|978-0-387-77609-5}}, 2008
A.J. Neighbor, J.J. Durillo, F. Luna, B. Dorronsoro, E. Alba, MOCell: A New Cellular Genetic Algorithm for Multiobjective Optimization, International Journal of Intelligent Systems, 24:726-746, 2009
E. Alba, B. Dorronsoro, F. Luna, A.J. Neighbor, P. Bouvry, L. Hogie, [http://www.lcc.uma.es/~eat/pdf/comcom07.pdf A Cellular Multi-Objective Genetic Algorithm for Optimal Broadcasting Strategy in Metropolitan MANETs], Computer Communications, 30(4):685-697, 2007
E. Alba, B. Dorronsoro, [http://atarazanas.sci.uma.es/docs/articulos/16603096.pdf Computing Nine New Best-So-Far Solutions for Capacitated VRP with a Cellular GA], Information Processing Letters, Elsevier, 98(6):225-230, 30 June 2006
M. Giacobini, M. Tomassini, A. Tettamanzi, E. Alba, [https://www.researchgate.net/profile/Andrea_Tettamanzi/publication/3418844_Selection_Intensity_in_Cellular_Evolutionary_Algorithms_for_Regular_Lattices/links/00463525c395463b1b000000/Selection-Intensity-in-Cellular-Evolutionary-Algorithms-for-Regular-Lattices.pdf The Selection Intensity in Cellular Evolutionary Algorithms for Regular Lattices], IEEE Transactions on Evolutionary Computation, IEEE Press, 9(5):489-505, 2005
E. Alba, B. Dorronsoro, [http://atarazanas.sci.uma.es/docs/tesisuma/1661480x.pdf The Exploration/Exploitation Tradeoff in Dynamic Cellular Genetic Algorithms], IEEE Transactions on Evolutionary Computation, IEEE Press, 9(2)126-142, 2005

External links

[http://neo.lcc.uma.es/cEA-web/ The site on Cellular Evolutionary Algorithms]
[http://neo.lcc.uma.es NEO Research Group at University of Málaga, Spain] {{Webarchive|url=https://web.archive.org/web/20180928161219/http://neo.lcc.uma.es/ |date=2018-09-28 }}

Category:Evolutionary algorithms