Estimation of Distribution Algorithms (EDAs) is a new area of Evolutionary Computation. In EDAs there is neither crossover nor mutation operators. New population is generated by sampling the probability distribution, which is estimated from a database containing selected individuals of the previous generation. Different approaches have been proposed for the estimation of probability distribution. In this paper we provide a review of different EDA approaches and show how to apply UMDA with Laplace correction to Subset Sum, OneMax function and n-Queen problems of linear and combinatorial optimizations. The experimental results of the three problems comparing the performance of UMDA with that of Genetic Algorithm(GA) are provided. In our experiment UMDA outperforms GA for linear problems.

One of the most important issues in software testing is the generation of the input cases used during the test. Due to the expensive cost of this task its automatization has become a key aspect.

Abstract. This paper discusses the performance of a hybrid system which consists of EDP and GP. EDP, Estimation of Distribution Programming, is the program evolution method based on the probabilistic model, where the probability distribution of a program is estimated by using a Bayesian network, and a population evolves repeating estimation of distribution and program generation without crossover and mutation. Applying the hybrid system of EDP and GP to various problems, we discovered some important tendencies in the behavior of this hybrid system. The hybrid system was not only superior to pure GP in a search performance but also had interesting features in program evolution. More tests revealed how and when EDP and GP compensate for each other. We show some experimental results of program evolution by the hybrid system and discuss the characteristics of both EDP and GP.

This paper proposes an algorithm for combinatorial optimizations that uses reinforcement learning and estimation of joint probability distribution of promising solutions to generate a new population of solutions. We call it Reinforcement Learning Estimation of Distribution Algorithm (RELEDA). For the estimation of the joint probability distribution we consider each variable as univariate. Then we update the probability of each variable by applying reinforcement learning method.

Recently, there has been a growing interest in classification of patient samples based on gene expressions. Here the classification task is made more difficult by the noisy nature of the data, and by the overwhelming number of genes relative to the number of available training samples in the data set. Moreover, many of these genes are irrelevant for classification and have negative effect on the accuracy and on the required learning time for the classifier. In this paper, we propose a new evolutionary computation method to select the most useful subset of genes for molecular classification. We apply this method to three benchmark data sets and present our unbiased experimental results. I.

In this technical report the simplest version of PBIL algorithm is applied to the minimization of the counting ones function in two dimensions.

Abstract- Axelrod’s original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent Population Based Incremental Learning system under similar conditions. We find that GA performs slightly better than standard PBIL under most conditions. This differnce in performance can be mitigated and reversed through the use of a ‘guided’ mutation operator. I.

Abstract — Most optimization algorithms that use probabilistic models focus on extracting the information from good solutions found in the population. A selection method discards the below-average solutions. They do not contribute any information to be used to update the models. This work proposes a new algorithm, Combinatorial Optimization with Coincidence (COIN) that makes use of both good and not-good solutions. A Generator represents a probabilistic model of the required solution, is used to sample candidate solutions. Reward and punishment schemes are incorporated in updating the generator. The updating values are defined by selecting the good and not-good solutions. It has been observed that the notgood solutions contribute to avoid producing the bad solutions. The multi-objective version of COIN is also introduced. Several benchmarks of multi-objective problems of real world industrial applications are reported. I.

Abstract. The Estimation of Distribution Algorithm is a new class of population based search methods in that a probabilistic model of individuals is estimated based on the high quality individuals and used to generate the new individuals. In this paper we compute 1) some upper bounds on the number of iterations required for global convergence of EDA 2) the exact number of iterations needed for EDA to converge to global optima. 1

Abstract — In the future a need will exist, if it does not already, to automate supply chains as trading electronically becomes increasingly important. Using the Simple Supply Chain Model (SSCM) allows a supply chain situation to be captured for experimentation. This paper describes efforts to evolve strategies for tackling SSCM specified problems through the use of a Strategy Framework (SSF) and Market Simulation System (SMSS). While the SSF provides a basic strategy representation system, the SMSS evolves strategies over multiple supply chain simulations using Population Based Incremental Learning with Guided Mutation. The paper further discuss some of the techniques being used to analyse the resultant data. I.