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.

Part 1: Fascinating Evolution -- Part 2: Evolutionary Computation -- Part 3: If Darwin was a Programmer -- Part 4: Evolution of Developmental Programs.

Art is the Queen of all sciences communicating knowledge to all the generations of the world. Leonardo da Vinci Artistic behavior is one of the most valued qualities of the human mind. Although artistic manifestations vary from culture to culture, dedication to artistic tasks is common to all. In other words, artistic behavior is a universal trait of the human species. The current, Western de?nition of art is relatively new. However, a d- ication to artistic endeavors — such as the embellishment of tools, body - namentation, or gathering of unusual, arguably aesthetic, objects — can be traced back to the origins of humanity. That is, art is ever-present in human history and prehistory. Artandsciencesharealongandenduringrelationship.Thebest-known- ample of the explorationof this relationship is probably the work of Leonardo da Vinci. Somewhere in the 19th century art and science grew apart, but the cross-transfer of concepts between the two domains continued to exist. Currently, albeit the need for specialization, there is a growing interest in the exploration of the connections between art and science. Focusingoncomputerscience,itisinterestingtonoticethatearlypioneers of this discipline such as Ada Byron and Alan Turing showed an interest in using computational devices for art-making purposes. Oddly, in spite of this early interest and the ubiquity of art, it has received relatively little attention fromthe computersciencecommunityingeneral,and,moresurprisingly,from the arti?cial intelligence community.

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.

The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionary 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 software engineering.

Agent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agement, business process management etc. There has been a significant growth in the number of publications related to the development and applications of agent based systems in recent years which has prompted special issues of journals and dedicated sessions in premier conferences. The notion of an agent with its ability to sense, learn and act autonomously - lows the development of a plethora of efficient algorithms to deal with complex problems. This notion of an agent differs significantly from a restrictive definition of a solution in an evolutionary algorithm and opens up the possibility to model and capture emergent behavior of complex systems through a natural age- oriented decomposition of the problem space. While this flexibility of represen- tion offered by agent based systems is widely acknowledged, they need to be - signed for specific purposes capturing the right level of details and description. This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems. There are 12 peer reviewed chapters in this book authored by d- tinguished researchers who have shared their experience and findings spanning across a wide range of applications.

This book constitutes the refereed proceedings of the First International Conference on Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoCOP, EvoBIO, and EvoApplications. Due to its significant growth in the last 10 years, this 10th EvoMUSART event has become an Evo* conference in 2012. The 15 revised full papers and 5 poster papers presented were carefully reviewed and selected from 43 submissions. They cover a wide range of topics reflecting the current state of research in the field, including theory, generation, computer aided creativity, computational creativity, and automation.