Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

In this paper, we propose a novel framework for the parallel solution of combinatorial problems based on MODS theory (PARMODS) This framework makes use of metaheuristics based on the Deterministic Swapping (MODS) theory. These approaches represents the feasible solution space of any combinatorial problem through a Deterministic Finite Automata. Some of those methods are the Metaheuristic Of Deterministic Swapping (MODS), the Simulated Annealing Deterministic Swapping (SAMODS), the Simulated Annealing Genetic Swapping (SAGAMODS) and the Evolutionary Deterministic Swapping (EMODS) Those approaches have been utilized in different contexts such as data base optimization, operational research [1-3, 8] and multi-objective optimization. The main idea of this framework is to exploit parallel computation in order to obtain a general view of the feasible solution space of any combinatorial optimization problem. This is, all the MODS methods are used in a unique general optimization process. In parallel, each instance of MODS explores a different region of the solution space. This allows us to explore distant regions of the feasible solution which could not be explored making use of classical (sequential) MODS implementations. Some experiments are performed making use of well-known TSP instances. Partial results shows that PARMODS provides better solutions than sequential MODS based implementations.

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A survey on some possibilities for applications of genetic algorithms in the automata theory

Bogdanović¹

2016

God Uci Fak Vranje

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One thing is for sure -the origin of life today, nature is very well aware of what's he is doing. Genetic algorithms (GA) are a family of algorithms that use some kind of genetic principles that are present in nature, in order to solve specific computational problems. Genetic algorithms are robust and adaptive methods in addition to other fields of application can be used for solving combinatorial optimization problems. The reason is that genetic algorithms can be relatively easily adapted to the specific problem. Automata have already been constructed by evolutional techniques in 60's of last century by Fogel. He created an "evolutional programming" that was the first attempt to construct an automaton by evolutionary approach. The aim of this paper was to further demonstrate the possibility of application of GA in the Automata theory.

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Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

Cited by 3 publications

References 71 publications

A Novel, Evolutionary, Simulated Annealing inspired Algorithm for the Multi-Objective Optimization of Combinatorial Problems

A Novel, Evolutionary, Simulated Annealing inspired Algorithm for the Multi-Objective Optimization of Combinatorial Problems

PARMODS: A Parallel Framework for MODS Metaheuristics

A survey on some possibilities for applications of genetic algorithms in the automata theory

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