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A Quantitative Approach for Validating the Buildingblock Hypothesis
"... Abstract The building blocks are common structures of highquality solutions. Genetic algorithms often assume the buildingblock hypothesis. It is hypothesized that the highquality solutions are composed of building blocks and the solution quality can be improved by composing building blocks. The ..."
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Abstract The building blocks are common structures of highquality solutions. Genetic algorithms often assume the buildingblock hypothesis. It is hypothesized that the highquality solutions are composed of building blocks and the solution quality can be improved by composing building blocks. The studies of building blocks are limited to some artificial optimization functions in which it is obvious that the building blocks exist. A large number of successful applications has been reported without a strong evidence that proves the hypothesis. This paper proposes a quantitative approach for validating the buildingblock hypothesis. We define the quantity of building blocks and the degree of discontinuity by using the chisquare matrix. We test the buildingblock hypothesis with 15bit onemax, 5×3trap, parabola 1 − (x 2 /10 10), and twodimensional Euclidian traveling salesman problem (TSP). The buildingblock hypothesis holds for onemax, 5×3trap, and parabola. In the case of parabola, Gray coding gives a higher quantity of building blocks than that of binary coding. The hypothesis is accepted for random instances of TSP with a low confidence. 1
The Use of Explicit Building Blocks in Evolutionary Computation
"... This paper proposes a new algorithm to identify and compose building blocks. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks from a population explicitly. Explicit building blocks are identified from shared alleles a ..."
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This paper proposes a new algorithm to identify and compose building blocks. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks from a population explicitly. Explicit building blocks are identified from shared alleles among multiple chromosomes. These building blocks are stored in an archive. They are recombined to generate offspring. The additively decomposable problems and hierarchical decomposable problems are used to validate the algorithm. The results are compared with the Bayesian Optimization Algorithm, the Hierarchical Bayesian Optimization Algorithm, and the Chisquare Matrix. This proposed algorithm is simple, effective, and fast. The experimental results confirm that building block identification is an important process that guides the recombination procedure to improve the solutions. In addition, the method efficiently solves hard problems.
Published by Pushpa Publishing House, Allahabad, INDIA USING CHISQUARE MATRIX TO STRENGTHEN MULTIOBJECTIVE EVOLUTIONARY ALGORITHM
"... Many complex engineering problems have multiobjectives where each objective is conflicting with others. However, a lot research ..."
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Many complex engineering problems have multiobjectives where each objective is conflicting with others. However, a lot research
Solving Additively Decomposable Functions by Building Blocks Identification
"... Abstract This paper proposes a way to use Building Blocks to improve solutions in Genetic Algorithm. Hard problems, for instance, Additively Decomposable Functions (ADFs) cannot be effectively solved by a standard algorithm such as Simple Genetic Algorithm. A single point crossover creates disrupti ..."
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Abstract This paper proposes a way to use Building Blocks to improve solutions in Genetic Algorithm. Hard problems, for instance, Additively Decomposable Functions (ADFs) cannot be effectively solved by a standard algorithm such as Simple Genetic Algorithm. A single point crossover creates disruption of good solutions for such problems. We proposed using Building Blocks Identification and performed appropriate crossover to solve ADFs. The experiment shows the validity of the proposed method. I.
Common Structures Identification for Solving 3D Bin Packing by Genetic Algorithm
"... Abstract—Recently, it is shown that genetic algorithms perform optimization by identifying and composing common structures of aboveaverage solutions. This paper aims to identify the quantity of building blocks in 3D bin packing problem. A solution can be encoded into a binary string by many differe ..."
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Abstract—Recently, it is shown that genetic algorithms perform optimization by identifying and composing common structures of aboveaverage solutions. This paper aims to identify the quantity of building blocks in 3D bin packing problem. A solution can be encoded into a binary string by many different coding. The results show that the quantities of building blocks are significantly different according to how the solutions are encoded. The coding that gives high quantity of building blocks yields better average fitness of solutions. As a result, we can spend a little time to predict the efficiency of a large number of coding by measuring the quantity of building blocks. I.
Contents lists available at ScienceDirect Applied Soft Computing
"... journal homepage: www.elsevier.com/locate/asoc Real options approach to evaluating genetic algorithms ..."
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journal homepage: www.elsevier.com/locate/asoc Real options approach to evaluating genetic algorithms
Algorithm, Hierarchical Bayesian Optimization Algorithm, and
"... Abstract — This paper proposes a new algorithm to identify and compose building blocks based on minimum mutual information criterion. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks in population explicitly. The addi ..."
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Abstract — This paper proposes a new algorithm to identify and compose building blocks based on minimum mutual information criterion. Building blocks are interpreted as common subsequences between good individuals. The proposed algorithm can extract building blocks in population explicitly. The additively decomposable problems and hierarchical decomposable problems are used to validate the algorithm. The results are compared with Bayesian Optimization