Evolution of cellular automata with memory: The Density Classification Task

Stone, Christopher; Bull, Larry

doi:10.1016/j.biosystems.2009.05.001

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Later, Martins and Oliveira discovered various couples and triples of rules that solve the problem when applied sequentially and for a fixed number of steps [MdO05] that depends on lattice size. Some authors also proposed to embed a memory in the cells to enhance the abilities of the rules [ASB09,SB09].…”

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confidence: 99%

Stochastic Cellular Automata Solutions to the Density Classification Problem

Fatès

2012

Theory Comput Syst

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In the density classification problem, a binary cellular automaton (CA) should decide whether an initial configuration contains more 0s or more 1s. The answer is given when all cells of the CA agree on a given state. This problem is known for having no exact solution in the case of binary deterministic one-dimensional CA. We investigate how randomness in CA may help us solve the problem. We analyse the behaviour of stochastic CA rules that perform the density classification task. We show that describing stochastic rules as a "blend" of deterministic rules allows us to derive quantitative results on the classification time and the classification time of previously studied rules. We introduce a new rule whose effect is to spread defects and to wash them out. This stochastic rule solves the problem with an arbitrary precision, that is, its quality of classification can be made arbitrarily high, though at the price of an increase of the convergence time. We experimentally demonstrate that this rule exhibits good scaling properties and that it attains qualities of classification never reached so far.

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confidence: 99%

Stochastic Cellular Automata Solutions to the Density Classification Problem

Fatès

2012

Theory Comput Syst

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“…For instance, probabilistic cellular automata [32] or cellular automata with memory [33,34]. Besides, other kind of complex systems could be analysed with these methods, as coupled map lattices and random boolean networks [32,35].…”

Section: Discussionmentioning

confidence: 99%

Emergence of density dynamics by surface interpolation in elementary cellular automata

Seck-Tuoh-Mora

Medina-Marín

Mart́ınez

et al. 2014

Communications in Nonlinear Science and Numerical Simulation

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“…It is applied to solve economic load dispatch and combined economic and emission load dispatch problems. Reference [21] investigates the evolving ability of a cellular automaton with a type of memory based on the least mean square algorithm. Reference [22] presents a memory-efficient stochastic evolution-based algorithm for solving multiobjective shortest path problems.…”

Section: Memory-based Evolutionary Algorithmsmentioning

confidence: 99%

“…Comparing their results and convergence, it is easy to observe the performance advantage of HMPSO over its competitors. The last three rows in Table III indicate that the numbers of functions for which HMPSO are better than CPSO-H6, CLPSO, ALCPSO, FIPS, HPSO-TVAC, EDA-PSO, and PSEDA are 21,18,21,25,22,27, and 27, respectively. The numbers of functions for which they are similar to 1.…”

Section: B Comparison With Seven Pso Algorithmsmentioning

confidence: 99%

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Zhang

Jiang

et al. 2015

IEEE Trans. Cybern.

153

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Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.

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Evolution of cellular automata with memory: The Density Classification Task

Cited by 12 publications

References 13 publications

Stochastic Cellular Automata Solutions to the Density Classification Problem

Stochastic Cellular Automata Solutions to the Density Classification Problem

Emergence of density dynamics by surface interpolation in elementary cellular automata

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

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