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Graph based crossover  A case study with the Busy Beaver problem
 Proceedings of the Genetic and Evolutionary Computation Conference
, 1999
"... The success of the application of evolutionary approaches depends, to a large extent, on problem representation and on the used genetic operators. In this paper we introduce a new graph based crossover operator and compare it with classical twopoint crossover. The study was carried out using a theo ..."
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Cited by 15 (4 self)
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The success of the application of evolutionary approaches depends, to a large extent, on problem representation and on the used genetic operators. In this paper we introduce a new graph based crossover operator and compare it with classical twopoint crossover. The study was carried out using a theoretical hard problem known as Busy Beaver. This problem involves the search for the Turing Machine that produces the maximum number of ones when started on a blank tape. Experimental results show that, in this domain, the new graphbased operator provides a clear advantage over twopoint crossover. 1
Busy Beaver  The Influence of Representation
 Proceedings of the Second European Workshop in Genetic Programming
, 1999
"... The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962, in the context of the existence of non computable functions. In this paper we propose an evolutionary approach to this problem. We will focus on the representational issues, proposing alternative ways of codifying and ..."
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Cited by 9 (4 self)
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The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962, in the context of the existence of non computable functions. In this paper we propose an evolutionary approach to this problem. We will focus on the representational issues, proposing alternative ways of codifying and interpreting Turing Machines, designed to take advantage of the existence of sets of equivalent Turing Machines. The experimental results show that these alternatives provide improvement over the "standard" genetic codification.
Too Busy to Learn
 In Proc. of the 2000 Congress on Evolutionary Computation, pages 720 727, Piscataway, NJ. IEEE Service
, 2000
"... The goal of this research is to analyze how individual learning interacts with an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, ..."
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Cited by 3 (1 self)
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The goal of this research is to analyze how individual learning interacts with an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular and prone to premature convergence search spaces, local search methods are not an effective help to evolution. In addition, one interesting effect related to learning is reported. When the mutation rate is too high, learning acts as a repair, reintroducing some useful information that was lost. 1 Introduction Evolution and learning are the two major forces that promote the adaptation of individuals to the environment. Each one of these complementary forces takes place at different levels. Evolution, operating at the population level, includes all mechanisms of genetic changes that occur in organisms over successive generations. Learning occurs at a different time scale...
Chapter II Evolutionary
"... In this chapter we study the feasibility of using Turing Machines as a model for the evolution of computer programs. To assess this idea we select, as test problem, the Busy Beaver — a wellknown theoretical problem of undisputed interest and difficulty proposed by Tibor Rado in 1962. We focus our r ..."
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In this chapter we study the feasibility of using Turing Machines as a model for the evolution of computer programs. To assess this idea we select, as test problem, the Busy Beaver — a wellknown theoretical problem of undisputed interest and difficulty proposed by Tibor Rado in 1962. We focus our research on representational issues and on the development of specific genetic operators, proposing alternative ways of encoding and manipulating Turing Machines. The results attained on a comprehensive set of experiments show that the proposed techniques bring significant performance improvements. Moreover, the use of a graph based crossover operator, in conjunction with new representation techniques, allowed us to establish new best candidates for the 6, 7, and 8 states instances of the 4tuple Busy Beaver problem.
A Genetic Algorithm for Finite State Machine Inference
, 2002
"... This thesis aims to improve the use of genetic algorithm in the problem of inferring a finite state machine which is consistent with a given input/output sequences set. The thesis uses the work of Aporntewan as a reference model and then analyzing the problem in Genetic Algorithm point of view to fi ..."
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This thesis aims to improve the use of genetic algorithm in the problem of inferring a finite state machine which is consistent with a given input/output sequences set. The thesis uses the work of Aporntewan as a reference model and then analyzing the problem in Genetic Algorithm point of view to find various ways to improve the method. The final goal is to find a better way to infer a finite state machine from a given input/output set 1