Previous papers have proposed an algorithm called the edge histogram sampling algorithm (EHBSA) that models the relative relation between two nodes (edge) of permutation strings of a population within the PMBGA framework for permutation domains. This paper proposes another histogram based model we call the node histogram sampling algorithm (NHBSA). The NHBSA models node frequencies at each absolute position in strings of a population. Sampling methods are similar to that of EHBSA. Performance of NHBSA is compared with that of EHBSA using two types of permutation problems: the FSSP and the quadratic assignment problem (QAP). The results showed that the NHBSA works better than the EHBSA on these problems. Keywords Probabilistic model-building genetic algorithms (PMBGAs), estimation of distribution algorithms (EDAs), permutation problems, edge histogram, node histogram, traveling salesman problem, flow shop scheduling problem, quadratic assignment problem.

In this paper, we propose a variant of an ACO algorithm called the cunning Ant System (cAS). In cAS, each ant generates a solution by borrowing a part of a solution which was generated in previous iterations, instead of generating the solution entirely from pheromone density. Thus we named it, cunning ant. This cunning action reduces premature stagnation and exhibits good performance in the search. The experimental results showed cAS worked very well on the TSP and it may be one of the most promising ACO algorithms.

The package JavaEvA (a Java implementation of Evolutionary Algorithms) is a general modular framework with an inherent client server structure to solve practical optimization problems. This package was especially designed to test and develop new approaches for Evolutionary Algorithms and to utilize them in real-world applications. JavaEvA already provides implementations of the most common Evolutionary Algorithms,

This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey. 1

Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic modeling. In this scheme, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operatom. In this paper, we have proposed probabilistic model-building genetic algo- rithms (PMBGAs) in permutation representation domain using edge histogram based sampling algorithms (EHBSAs). Two types of sampling algorithms, without template (EHBSA/WO) and with template (EHBSA/WT), are presented. The results were tested in the TSP and showed EHBSA/WT worked fairly well with a small population size in the test problems used. It also worked better than well-known traditional two-parent recombination operators.

This article compares two edge-based Estimation of Distribution Algorithms named Edge Histogram Based Sampling Algorithm (EHBSA) and Coincidence Algorithm (COIN) in multimodal combinatorial puzzles benchmarks. Both EHBSA and COIN make use of joint probability matrix of adjacent events (edge) derived from the population of candidate solutions. These algorithms are expected to be competitive in solving problems where relative relation between two nodes is significant. The experiment results imply that EHBSAs are better in convergence to a single optima point, while COINs are better in maintaining the diversity among the population and are better in preventing the premature convergence.

In evolutionary algorithms based on probabilistic modeling, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. In this chapter, we have proposed a probabilistic model-building genetic algorithms (PMBGAs) for solving flow shop scheduling problems using edge histogram based sampling algorithms (EHBSAs). The effectiveness of introducing the tag node (TN) in a string representation is also discussed. 1.

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This chapter serves as an introduction to estimation of distribution algorithms. Estimation of distribution algorithms are a new paradigm in evolutionary computa-tion. State-of-the-art EDAs consistently outperform classical genetic algorithms on a broad range of problems. We review the fundamental principles and algorithms that are necessary to understand EDA research. We focus on EDAs for the discrete and the continuous problem domains and discuss the differences between the two.