New Achievements in Evolutionary Computation

Abstract

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 bioinspired. 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.