This book constitutes the refereed proceedings of the International Conference on Evolutionary Computation held jointly with the 4th Conference on Parallel Problem Solving from Nature, PPSN IV, in Berlin, Germany, in September 1996. The 103 revised papers presented in the volume were carefully selected from more than 160 submissions. The papers are organized in sections on basic concepts of evolutionary computation (EC), theoretical foundations of EC, modifications and extensions of evolutionary algorithms, comparison of methods, other metaphors, and applications of EC in a variety of areas like ML, NNs, engineering, CS, OR, and biology. The book has a comprehensive subject index.

This book constitutes the refereed proceedings of the 5th International Conference on Parallel Problem Solving from Nature, PPSN V, held in Amsterdam, The Netherlands, in September 1998. The 101 papers included in their revised form were carefully reviewed and selected from a total of 185 submissions. The book is divided into topical sections on convergence theory; fitness landscape and problem difficulty; noisy and non-stationary objective functions; multi-criteria and constrained optimization; representative issues; selection, operators, and evolution schemes; coevolution and learning; cellular automata, fuzzy systems, and neural networks; ant colonies, immune systems, and other paradigms; TSP, graphs, and satisfiability; scheduling, partitioning, and packing; design and telecommunications; and model estimations and layout problems.

This book on bioinformatics is designed as an introduction to the conventional details of genomics and proteomics as well as a practical comprehension text with an extended scope on the state-of-the-art bioinformatic details pertinent to next-generation sequencing, translational/clinical bioinformatics and vaccine-design related viral informatics.It includes four major sections: (i) An introduction to bioinformatics with a focus on the fundamentals of information-theory applied to biology/microbiology, with notes on bioinformatic resources, data bases, information networking and tools; (ii) a collection of annotations on the analytics of biomolecular sequences, with pertinent details presented on biomolecular informatics, pairwise and multiple sequences, viral sequence informatics, next-generation sequencing and translational/clinical bioinformatics; (iii) a novel section on cytogenetic and organelle bioinformatics explaining the entropy-theoretics of cellular structures and the underlying informatics of synteny correlations; and (iv) a comprehensive presentation on phylogeny and species informatics.The book is aimed at students, faculty and researchers in biology, health/medical sciences, veterinary/agricultural sciences, bioengineering, biotechnology and genetic engineering. It will be a useful companion for managerial personnel in the biotechnology and bioengineering industries as well as in health/medical science.

Printed Edition of the Special Issue Published in Entropy

This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.