Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm

Bäck, Thomas; Breukelaar, Ron; Willmes, Lars

doi:10.1007/11527800_13

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The fitness function is directly related to the error measure E I defined by (9). Although Proposition 7 states that the error measures given by (6) and (9) can be used interchangeably, preliminary experiments showed that the later results in efficient and convergent algorithm, while suboptimal results were obtained using the measure given by (6). This follows from the fact that the error in row n is affected by errors appearing in rows 2, .…”

Section: B Fitness Functionmentioning

confidence: 99%

An evolutionary approach to the identification of Cellular Automata based on partial observations

Bołt

Baetens

Baets

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-In this paper we consider the identification problem of Cellular Automata (CAs). The problem is defined and solved in the context of partial observations with time gaps of unknown length, i.e. pre-recorded, partial configurations of the system at certain, unknown time steps. A solution method based on a modified variant of a Genetic Algorithm (GA) is proposed and illustrated with brief experimental results.

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Section: B Fitness Functionmentioning

confidence: 99%

An evolutionary approach to the identification of Cellular Automata based on partial observations

Bołt

Baetens

Baets

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Using Genetic Algorithms to Evolve Behavior in Cellular Automata

Bäck

Breukelaar

2005

Lecture Notes in Computer Science

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Abstract.It is an unconventional computation approach to evolve solutions instead of calculating them. Although using evolutionary computation in computer science dates back to the 1960s, using an evolutionary approach to program other algorithms is not that well known. In this paper a genetic algorithm is used to evolve behavior in cellular automata. It shows how this approach works for different topologies and neighborhood shapes. Some different one dimensional neighborhood shapes are investigated with the genetic algorithm and yield surprisingly good results.

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On the Identification of $$\alpha $$ α -Asynchronous Cellular Automata in the Case of Partial Observations with Spatially Separated Gaps

Bołt

Wolnik

Baetens

et al. 2016

Challenging Problems and Solutions in Intelligent Systems

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Inverse Design of Cellular Automata by Genetic Algorithms: An Unconventional Programming Paradigm

Cited by 4 publications

References 3 publications

An evolutionary approach to the identification of Cellular Automata based on partial observations

An evolutionary approach to the identification of Cellular Automata based on partial observations

Using Genetic Algorithms to Evolve Behavior in Cellular Automata

On the Identification of $$\alpha $$ α -Asynchronous Cellular Automata in the Case of Partial Observations with Spatially Separated Gaps

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