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Success in Evolutionary Computation

Darwinian evolutionary theory is one of the most important theories in human history for it has equipped us with a valuable tool to understand the amazing world around us. There can be little surprise, therefore, that Evolutionary Computation (EC), inspired by natural evolution, has been so successful in providing high quality solutions in a large number of domains. EC includes a number of techniques, such as Genetic Algorithms, Genetic Programming, Evolution Strategy and Evolutionary Programming, which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting rapidly evolving field.

Optimizing Multiplicative General Parameter Finite Impulse Response Filters
Using Evolutionary Computation Jarno Martikainen1 and Seppo J. Ovaska2 1
Helsinki University of Technology, Department of Electrical and Communications
 ...

Evolutionary Computation in Data Mining

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms.

Swarm and Evolutionary computation

International Symposium, SIDE 2012, held in Conjunction with ICAISC 2012, Zakopane, Poland, April 29 - May 3, 2012, Proceedings

The volume LNCS 7269 constitutes the refereed proceedings of the International Symposium on Swarm Intelligence and Differential Evolution, SIDE 2012, held in Zakopane, Poland, in April/May 2012 in conjunction with the 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2012 (proceedings published as two-volume set LNAI 7267 and 7268). The 212 revised full papers presented were carefully reviewed and selected from 483 submissions. The volume is divided into two topical parts: proceedings of the 2012 symposium on swarm intelligence and differential evolution and on evolutionary algorithms and their applications.

Comparison of ABC and ACO Algorithms Applied for Solving the Inverse Heat
Conduction Problem Edyta Hetmaniok, Damian Slota, Adam Zielonka, and
Roman Witula Institute of Mathematics, Silesian University of Technology,
Kaszubska 23 ...

Evolutionary Computation in Combinatorial Optimization

11th European Conference, EvoCOP 2011, Torino, Italy, April 27-29, 2011, Proceedings

This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

Prominent examples of metaheuristics are evolutionary algorithms, tabu search,
simulated annealing, scatter search, memetic algorithms, variable neighborhood
search, iterated local search, greedy randomized adaptive search procedures, ...

Evolutionary Computation in Combinatorial Optimization

12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012, Proceedings

This book constitutes the refereed proceedings of the 12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoBIO, EvoMUSART, and EvoApplications. . The 22 revised full papers presented were carefully reviewed and selected from 48 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.

Prominent examples of metaheuristics are evolutionary algorithms, tabu search,
simulated annealing, scatter search, memetic algorithms, variable neighborhood
search, iterated local search, greedy randomized adaptive search procedures, ...

Illustrating Evolutionary Computation with Mathematica

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject. * Introduces the major mechanisms of biological evolution. * Demonstrates many fascinating aspects of evolution in nature with simple, yet illustrative examples. * Explains each of the major branches of evolutionary computation: genetic algorithms, genetic programming, evolutionary programming, and evolution strategies. * Demonstrates the programming of computers by evolutionary principles using Evolvica, a genetic programming system designed by the author. * Shows in detail how to evolve developmental programs modeled by cellular automata and Lindenmayer systems. * Provides Mathematica notebooks on the Web that include all the programs in the book and supporting animations, movies, and graphics.

In practical applications, genetic algorithms are applied in combination with local
hill-climbing procedures. The genetic ... Books on evolutionary computation The
Handhoo/2 of Evolutionary Computation (Back, Fogel, et al. 1997) is a large ...

Principal Concepts in Applied Evolutionary Computation: Emerging Trends

Emerging Trends

Increasingly powerful and diverse computing technologies have the potential to tackle ever greater and more complex problems and dilemmas in engineering and science disciplines. Principal Concepts in Applied Evolutionary Computation: Emerging Trends provides an introduction to the important interdisciplinary discipline of evolutionary computation, an artificial intelligence field that combines the principles of computational intelligence with the mechanisms of the theory of evolution. Academics and practicing field professionals will find this reference useful as they break into the emerging and complex world of evolutionary computation, learning to harness and utilize this exciting new interdisciplinary field.

This book contains articles from the four issues of Volume 1 of the International
Journal of Applied Evolutionary Computation (IJAEC). As mentioned in journal's
description, this book reflects the journal's mission of publishing high-quality ...

Evolutionary Computation in Combinatorial Optimization

7th European Conference, EvoCOP 2007, Valencia, Spain, April 11-13, 2007, Proceedings

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers presented were carefully reviewed and selected from 81 submissions. The papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, greedy randomized adaptive search procedures, ant colony optimization, and particle swarm optimization algorithms. The papers are specifically dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems and cover any issue of metaheuristic for combinatorial optimization. The papers deal with representations, heuristics, analysis of problem structures, and comparisons of algorithms. The list of studied combinatorial optimization problems includes prominent examples like graph coloring, knapsack problems, the traveling salesperson problem, scheduling, graph matching, as well as specific real-world problems.

Prominent examples of metaheuristics are evolutionary algorithms, simulated
annealing, tabu search, scatter search, memetic algorithms, variable
neighborhood search, iterated local search, greedy randomized adaptive search
procedures, ...

Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques

Applications and Techniques

Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques lays the foundation for the successful integration of evolutionary computation into software engineering. It surveys techniques ranging from genetic algorithms, to swarm optimization theory, to ant colony optimization, demonstrating their uses and capabilities. These techniques are applied to aspects of software engineering such as software testing, quality assessment, reliability assessment, and fault prediction models, among others, to providing researchers, scholars and students with the knowledge needed to expand this burgeoning application.

Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques lays the foundation for the successful integration of evolutionary computation into software engineering.

Genetic and Evolutionary Computation--GECCO 2003

Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003 : Proceedings

The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionaty Computation Conference, GECCO 2003, held in Chicago, IL, USA in July 2003. The 193 revised full papers and 93 poster papers presented were carefully reviewed and selected from a total of 417 submissions. The papers are organized in topical sections on a-life adaptive behavior, agents, and ant colony optimization; artificial immune systems; coevolution; DNA, molecular, and quantum computing; evolvable hardware; evolutionary robotics; evolution strategies and evolutionary programming; evolutionary sheduling routing; genetic algorithms; genetic programming; learning classifier systems; real-world applications; and search based softare engineering.

Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16
, 2003 : Proceedings Erick Cantú-Paz ... We explore the advantages of DNA-like
genomes for evolutionary computation in silico. Coupled with simulations of ...