Using Genetic Algorithms to Evolve Behavior in Cellular Automata

Bäck, Thomas; Breukelaar, Ron

doi:10.1007/11560319_1

Cited by 17 publications

(7 citation statements)

References 7 publications

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“…Still, we mention several characteristic works where EAs are used to evolve shift-invariant transformations or related objects. Bäck and Breukelaar used genetic algorithms to evolve behavior in CA where the authors explored different neighborhood shapes [1]. Sipper and Tomassini [16] proposed a cellular programming algorithm to co-evolve the rule map of non-uniform CA for designing random number generators.…”

Section: Related Workmentioning

confidence: 99%

“…In this regard, Evolutionary Algorithms (EAs) represent an interesting method to investigate known RCA classes concerning these additional design criteria, since exhaustively searching for all possible RCA becomes unfeasible for large local rule sizes. To the best of our knowledge, this research method has not been pursued before, although some authors employed EA to evolve CA featuring certain properties other than reversibility [1,11].…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary View on Reversible Shift-Invariant Transformations

Mariot

Picek

Jakobović

et al. 2020

Lecture Notes in Computer Science

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We consider the problem of evolving a particular kind of shift-invariant transformation-namely, Reversible Cellular Automata (RCA) defined by conserved landscape rules-using GA and GP. To this end, we employ three different optimization strategies: a single-objective approach carried out with GA and GP where only the reversibility constraint of marker CA is considered, a multi-objective approach based on GP where both reversibility and the Hamming weight are taken into account, and a lexicographic approach where GP first optimizes only the reversibility property until a conserved landscape rule is obtained, and then maximizes the Hamming weight while retaining reversibility. The results are discussed in the context of three different research questions stemming from exhaustive search experiments on conserved landscape CA, which concern 1) the difficulty of the associated optimization problem for GA and GP, 2) the utility of conserved landscape CA in the domain of cryptography and reversible computing, and 3) the relationship between the reversibility property and the Hamming weight.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evolutionary View on Reversible Shift-Invariant Transformations

Mariot

Picek

Jakobović

et al. 2020

Lecture Notes in Computer Science

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“…For a somewhat outdated but very detailed overview of works using GA to evolve CA, we refer interested readers to [22]. Bäck and Breukelaar used genetic algorithms to evolve behavior in CA and explored different neighborhood shapes [38]. The authors showed that their approach works for different topologies and neighborhood shapes.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary algorithms for designing reversible cellular automata

Mariot

Picek

Jakobović

et al. 2021

Genet Program Evolvable Mach

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Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by dynamics composed only of disjoint cycles. They have many applications in the simulation of physical systems, cryptography, and reversible computing. In this work, we formulate the search of a specific class of RCA – namely, those whose local update rules are defined by conserved landscapes – as an optimization problem to be tackled with Genetic Algorithms (GA) and Genetic Programming (GP). In particular, our experimental investigation revolves around three different research questions, which we address through a single-objective, a multi-objective, and a lexicographic approach. In the single-objective approach, we observe that GP can already find an optimal solution in the initial population. This indicates that evolutionary algorithms are not needed when evolving only the reversibility of such CA, and a more efficient method is to generate at random syntactic trees that define the local update rule. On the other hand, GA and GP proved to be quite effective in the multi-objective and lexicographic approach to (1) discover a trade-off between the reversibility and the Hamming weight of conserved landscape rules, and (2) observe that conserved landscape CA cannot be used in symmetric cryptography because their Hamming weight (and thus their nonlinearity) is too low.

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“…For instance, in [21], [30], [41], evolutionary cellular automata have been used for studying and describing strategies of the prisoner's dilemma game. In [3], the authors studied the use of genetic algorithms for finding rules of cellular automata such that they display a desired behavior focusing on the case of solving the majority problem with cellular automata. Also in [24] and [25], the authors exploited genetic algorithms for evolving cellular automata for specific computational tasks such as density classification and synchronization.…”

Section: Introductionmentioning

confidence: 99%

A symbiosis between cellular automata and genetic algorithms

Cerruti

Dutto

Murru

2020

Chaos, Solitons & Fractals

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Cellular automata are systems which use a rule to describe the evolution of a population in a discrete lattice, while genetic algorithms are procedures designed to find solutions to optimization problems inspired by the process of natural selection. In this paper, we introduce an original implementation of a cellular automaton whose rules use a fitness function to select for each cell the best mate to reproduce and a crossover operator to determine the resulting offspring. This new system, with a proper definition, can be both a cellular automaton and a genetic algorithm. We show that in our system the Conway's Game of Life can be easily implemented and, consequently, it is capable of universal computing. Moreover two generalizations of the Game of Life are created and also implemented with it. Finally, we use our system for studying and implementing the prisoner's dilemma and rock-paper-scissors games, showing very interesting behaviors and configurations (e.g., gliders) inside these games.

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Using Genetic Algorithms to Evolve Behavior in Cellular Automata

Cited by 17 publications

References 7 publications

An Evolutionary View on Reversible Shift-Invariant Transformations

An Evolutionary View on Reversible Shift-Invariant Transformations

Evolutionary algorithms for designing reversible cellular automata

A symbiosis between cellular automata and genetic algorithms

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