New Achievements in Evolutionary Computation New Achievements in Evolutionary Computation Edited by Peter Korosec Intech IV Published by Intech Intech Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the Intech, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2010 Intech Free online edition of this book you can find under www.sciyo.com Additional copies can be obtained from: publication@sciyo.com First published February 2010 Printed in India Technical Editor: Teodora Smiljanic Cover designed by Dino Smrekar New Achievements in Evolutionary Computation, Edited by Peter Korosec p. cm. ISBN 978-953-307-053-7 Preface Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored. However, even with so much work done, novel research into new techniques and new areas of usage is far from over. This book presents some new theoretical as well as practical aspects of evolutionary computation. The first part of the book is mainly concentrated on evolutionary algorithms and their applications. First, the influence that diversity has on evolutionary algorithms will be described. There is also an insight into how to efficiently solve the constraint-satisfaction problem and how time series can be determined by the use of evolutionary forecasting. Quantum finite-state machines are becoming increasingly more important. Here, an evolutionary-based logic is used for its synthesis. With an ever increasing number of criteria being used to evaluate a solution, this is leading to different multi-objective evolutionary approaches. Such approaches are being applied to control optimization and phylogenetic reconstruction. It is well known that evolutionary-computation approaches are mostly bio- inspired. So it is interesting to see how they can return to its origin by solving bio-problems. Here, they are used for predicting membrane protein-protein interactions and are applied to different bioinformatics applications. The second part of the book presents some other well-known evolutionary approaches, like genetic algorithms, genetic programming, estimations of the distribution algorithm, and swarm intelligence. Genetic algorithms are used in Q-learning to develop a compact control table, while flight-control system design is being optimized by genetic programming. A new estimation of the distribution algorithm, using the empirical selection distribution, is being presented and, on the other hand, a classical version is being applied to the video-tracking system problem. The book ends with the recently very popular swarm-intelligence approaches, where they are used in artificial societies, social simulations, and applied to the Chinese traveling-salesman problem. This book will be of great value to undergraduates, graduate students, researchers in computer science, and anyone else with an interest in learning about the latest developments in evolutionary computation. Editor Peter Korosec Contents Preface V 1. Diversity-Based Adaptive Evolutionary Algorithms 001 Maury Meirelles Gouvêa Jr. and Aluizio Fausto Ribeiro Araújo 2. Evolutionary Computation in Constraint Satisfaction 017 Madalina Ionita, Mihaela Breaban and Cornelius Croitoru 3. Morphological-Rank-Linear Models for Financial Time Series Forecasting 037 Ricardo de A. Araújo, Gláucio G. de M. Melo, Adriano L. I. de Oliveira and Sergio C. B. Soares 4. Evolutionary Logic Synthesis of Quantum Finite State Machines for Sequence Detection 077 Martin Lukac and Marek Perkowski 5. Conflicting Multi-Objective Compatible Optimization Control 113 Lihong Xu, Qingsong Hu, Haigen Hu and Erik Goodman 6. A Multi-Criterion Evolutionary Approach Applied to Phylogenetic Reconstruction 135 W. Cancino and A.C.B. Delbem 7. New Perspectives in Predicting Membrane Protein-protein Interactions 157 X. Zhang and B.F. Francis Ouellette 8. Evolutionary Computation Applications in Current Bioinformatics 173 Bing Wang and Xiang Zhang 9. GA-Based Q-Learning to Develop Compact Control Table for Multiple Agents 181 Tadahiko Murata and Yusuke Aoki VIII 10. Genetic Programming in Application to Flight Control System Design Optimisation 195 Anna Bourmistrova and Sergey Khantsis 11. Efficient Estimation of Distribution Algorithms by using the Empirical Selection Distribution 229 S. Ivvan Valdez, Arturo Hernández and Salvador Botello 12. Solving Combinatorial Problems with Time Constrains using Estimation of Distribution Algorithms and Their Application in Video-Tracking Systems 251 Antonio Berlanga, Miguel A. Patricio, Jesús García, and José M. Molina 13. Artificial Societies and Social Simulation using Ant Colony, Particle Swarm Optimization and Cultural Algorithms 267 Alberto Ochoa, Arturo Hernández, Laura Cruz, Julio Ponce, Fernando Montes, Liang Li and Lenka Janacek 14. Particle Swarm and Ant Colony Algorithms and Their Applications in Chinese Traveling Salesman Problem 297 Shuang Cong, Yajun Jia and Ke Deng 1 Diversity-Based Adaptive Evolutionary Algorithms Maury Meirelles Gouvêa Jr. and Aluizio Fausto Ribeiro Araújo Pontifical Catholic University of Minas Gerais Federal University of Pernambuco Brazil 1. Introduction In evolutionary algorithms (EAs), preserving the diversity of the population, or minimizing its loss, may benefit the evolutionary process in several ways, such as, by preventing premature convergence, by allocating the population in distinct Pareto optimal solutions in a multi objective problem, and by permitting fast adaptation in dynamic problems. Premature convergence may lead the EA to a non-optimal result, that is, converging to a local optimum. In static problems, standard EAs work well. However, many real world problems are dynamic or other uncertainties have to be taken into account, such as noise and fitness approximation. In dynamic problems, the preservation of diversity is a crucial issue because EAs need to explore the largest number of regions possible. Standard genetic algorithms (SGA) are not suitable for solving dynamic problems because their population quickly converges to a specific region of the solution space. The loss of diversity is caused by selection pressure and genetic drift, two factors inherent in EAs. The loss of diversity may lead the EA to a non-optimal result, despite the fact that after a period of time, EA tends to find the global optimum. In static problems, loss of diversity might not be a very critical problem. However in dynamic environments lack of diversity may degrade EA performance. Especially in dynamic problems, the preservation of diversity is a crucial issue because an EA needs to explore the search space aggressively. One option for reacting to a change of the environment is to consider each change as the arrival of a new optimization problem to be solved. This is a viable alternative if there is time available to solve the problem. However, the time available for finding the new optimum may be short and also sometimes the algorithm cannot identify the environmental change. When the new optimum is close to the old one, the search can be restricted to the neighborhood of the previous optimum. Thus, some knowledge about the previous search space can be used. However, reusing information from the past may not be promising depending on the nature of the change. If the change is large or unpredictable, restarting the search may be the only viable option. The approaches that handle dynamic environments, addressing the issue of convergence, can be divided into the following categories (Jin & Branke, 2005): (i) generating diversity after a change, (ii) preserving diversity throughout the run, (iii) memory-based approaches, and (iv) multi-population approaches. The first two approaches cover the diversity problem. New Achievements in Evolutionary Computation 2 In (i), an EA runs in standard way, but when a change is detected, some actions are taken to increase diversity. In (ii), convergence is avoided all the time and it is expected that a more dispersive population can adapt to changes. In (iii), EA is supplied with a memory so as to be able to recall useful information from past generations. In (iv), the population is divided into several subpopulations allowing different peaks in the environment to be tracked. The preservation of diversity has advantages that can be supported by theory, such as those cited above, and from Nature. The loss of diversity because of the extinction of species may produce irreversible ecological disturbance for an ecosystem. A high diversity level produces abilities which allow populations or species to react against adversities, such as diseases, parasites, and predators. An appropriate level of diversity allows populations or species to adapt to environmental changes. On the other hand, a low diversity level tends to limit these abilities (Amos & Harwood, 1998). From the point of view of the evolutionary process, the loss of diversity also represents serious problems, such as population convergence to a specific region of the solutions space; thus, EA losses its main feature, the global search. In order to preserve the diversity of the population it is necessary to create strategies to adjust one or more EA parameters, such as the mutation rate, selection pressure, etc. These strategies are known as diversity-based algorithms. This chapter presents a survey on diversity-based evolutionary algorithms. Two classes of models are presented: one to minimize the loss of diversity and another to control population diversity based on the desired diversity range or level. Several methods to measure the diversity of the population and the species are presented as a foundation for diversity control methods. The rest of this paper is organized as follows. Section 2 presents parameter setting and control in EAs. Section 3 describes several methods for measuring diversity. Section 4 presents methods to preserve and control population diversity in evolutionary algorithms. Finally, Section 5 concludes this chapter. 2. Parameter tuning and control in evolutionary computation The EA parameters can affect population diversity directly. For instance, a larger mutation rate causes disturbances in the offspring and, consequently, increases the diversity of the population in the next generation. On the other hand, the greater the selection pressure is, the fittest individuals tend to survive or generate more offspring. Thus, these individuals tend to be genetically similar, thus decreasing the diversity of the population. We can set parameter values by parameter tuning and parameter control (Angeline, 1995; Eiben et al., 1999; Hinterding et al., 1997). Parameter tuning finds appropriate values for the parameters before the algorithm is used, and these parameters are fixed during the run. For example, Bäck & Schutz (1996) suggest the following mutation probability LN p m 75.1 = , (1) where N is the population size and L is the individual length. Parameter control changes parameter values on-line in accordance with three categories (Eiben et al., 1999; Hinterding et al., 1997): deterministic, adaptive, and self-adaptive control methods. The next three subsections present these categories. [...]... operate in the same way: they maintain a population of individuals (particles, agents) which is updated by applying some operators according to the fitness information, in order to reach better solution areas The most known evolutionary computation paradigms include evolutionary algorithms (Genetic Algorithms, Genetic Programming, Evolutionary Strategies, Evolutionary Programming) and swarm intelligence... approach maintains a population diversity level by replacing some individuals from the current population by random individuals, called random immigrants, in every generation There are two ways that define how individuals are replaced: replacing individuals at random or replacing the worst ones (Vavak et al., 1996) RIGA inserts random individuals into the population, a strategy that may increase population... constraints (Liepins & Vose, 1990) The repaired solution is used only for evaluation or can replace with some probability the original individual 22 New Achievements in Evolutionary Computation • separation of objectives and constraints The constraints and the objectives are handled separately For example, in (Paredis, 1994) a co -evolutionary model consisting of two populations, one of constraints,... to obtain good results for a problem we have to incorporate knowledge about the problem into the evolutionary algorithm Evolutionary algorithms are flexible and can be Evolutionary Computation in Constraint Satisfaction 23 easily extended by incorporating standard procedures for the problem under investigation The heuristic information introduced in an evolutionary algorithm can enhance the exploitation... population 8 New Achievements in Evolutionary Computation is in a local or global optimum If the selection pressure is high, the best individuals near the best one tend to rise and survive in larger numbers, thus causing premature convergence Shimodaira tried to solve this problem by reducing appropriately the selection pressure in the neighborhood of the best individual to eliminate individuals similar... =1 (14) 6 New Achievements in Evolutionary Computation The diversity between species and population diversity have different characteristics In the former, the species are always different, whereas in the latter, two individuals may be genetically equal In the diversity of species, a new individual added to a set S increases its diversity In populations, a new individual may increase or decrease diversity... algorithm In (Eiben et al., 1994) heuristics are incorporated into the genetic operators The mutation operator selects a number of variables to be mutated and assigns them new values The selected variables are those appearing in constraints that are most often violated The new values are those that maximize the number of satisfied constraints Another way of incorporating heuristic information in an evolutionary. .. Algorithms in Dynamic Environment, 2007 IEEE Congress on Evolutionary Computation (CEC'2007), Singapore, South Korea, September 25-28, IEEE Press Hardy, G H (1908) Mendelian proportions in a mixed population Science, Vol 78, 49—50 Hinterding, R.; Michalewicz, Z.; Eiben, A E (1997) Adaptation in evolutionary computation: a survey, Proceedings of the 4th IEEE Conference Evolutionary Computation, pp 65—69, Indianapolis,... group (swarm) of particles moving in the search space, their trajectory being determined by the fitness values found so far Evolutionary Computation in Constraint Satisfaction 25 The formulas used to actualize the individuals and the procedures are inspired from and conceived for continuous spaces Each particle is represented by a vector x of length n indicating the position in the n-dimensional search... solvable problem instances: 25 instances for different values of p in model E(20, 20, p,2) Parameter p takes the following values: {0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30} All instances considered were solvable 5.2 Algorithms settings The variable ordering used in MBE was determined with the min-induced-width heuristic This method places the variable with the minimum degree last in the ordering It connects . New Achievements in Evolutionary Computation New Achievements in Evolutionary Computation Edited by Peter Korosec Intech IV Published by Intech. forecasting. Quantum finite-state machines are becoming increasingly more important. Here, an evolutionary- based logic is used for its synthesis. With an ever increasing number of criteria being. whereas in the latter, two individuals may be genetically equal. In the diversity of species, a new individual added to a set S increases its diversity. In populations, a new individual may increase