Required Texts
Dawkins,Richards, The Selfish Gene, Oxford, U.K.: Oxford University Press, 1989.
This revolutionary textbook propounds the author’s theory of neo-Darwinism. In it, the author discusses how the actual proponents of evolution are genes, not organisms as many assume. The theory that selfishness must develop, the author explores and develops in depth.
Kinnear, Kenneth E. Jr. Advances in Genetic Programming. Cambridge, Massachusetts: MIT Press. 1994.
This text examines a wide range of methods for improving genetic algorithm performance and suggests a number of current projects that genetic algorithms are suited for. The material is structured into three parts. Part I places genetic programming in the context of evolutionary computing , part II contains contributions to improving the genetic programming paradigm and part III presents a wide variety of applications of genetic programming.
Mitchell, Melanie. An Introduction to Genetic Algorithms, Cambridge, Massachusetts:MIT Press, 1996.
This textbook covers genetic algorithms, both in their theory and their implementation. Written by a Brown graduate, this text examines a number case studies of genetic algorithms and covers the terminology of the field as well as
the theory behind it.
Paton, Ray. Computing With Biological Metaphors. Ed. Ray Paton. London, UK: Chapman & Hall. 1994.
This textbook examines a wide variety of topics which combine biology and computer science.The book is organized into five parts : cells, tissues, genetics, ecology and conceptual issues. Both the formal , mathematical modelling approach and the imaginative , often analogical in nature one are found in this book because both types are accepted as valid within sphere of application.
Course Packet
Bilchev, G., and Parmee I.C."The Ant Colony Metaphor for Searching Continuous Design Spaces", Evolutionary Computing. Sheffield, U.K: 1995.
This article explores an ant colony's behavior as an example of a dynamic system's evolution.
David, F.N. A First Course in Statistics, New York: Hafner Publishing Company: 1971. 5 -23. 53-91. 110-125.
This book explains with a minimum of formulae and a maximum of practical examples the most common statistical methods in use today.
Langton, Christopher G., Artificial Life: An Overview. Cambridge, Massachusetts: MIT Press. 1995.
The book addresses behavior-oriented Artificial Intelligence as a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive.
Dennet, Daniel. “Artificial Life as Philosophy.” 291-292.
Dyer, Michael G. “Toward Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life.” 111-134.
Kaneko, Kunihiko. “Chaos as a Source of Complexity and Diversity in Evolution.” 163-178.
Lindgren, Kristian and Mats G. Nordahl. “Cooperation and Community Structure in Artificial Ecosystems.” 15-38.
Maes, Pattie. “Modeling Adaptive Autonomous Agents.” 135-162.
Schuster, P. “Extended Molecular Evolutionary Biology: Artificial Life Bridging the Gap Between Chemistry and Biology.” 39-60.
Steels, Luc. “The Artificial Life Roots of Artificial Intelligence.” 75-110.
Livi, R. and Ruffo, S., Chaos and Complexity, Singapore: World Scientific Publishing. 1988.
This article argues that spontaneous order in complex systems implies that selection may not be the sole source of order in organisms and that we should invent a new theory of evolution which encompasses the marriage of selection and self organization.
Buiatti, Marcello. “Information Flux and Constraints in Development and Evolution: A Critical Review.” 331-348.
Kauffman, Stuart A. “Origins of Order in Evolution: Self Organizing and Selection.” 349-387.
Vichniac, Gerard Y. “Cellular Automata and Complex Systems.” 263-275.
Miller, Normal. A First Course in Differential Equations, Oxford: Oxford University Press: 1939. 1- 34. 46-62. 76-84.
This book presents in a clear and concise manner the most useful methods of solving first and higher order differential equations , emphasizing their applications.
Patel, Mukesh J., "Constraints on Task and Search Complexity in Genetic Algorithms and Neural Networks models of Learning and adaptive behavior.", Evolutionary Computing. Sheffield, U.K: 1995.
This article explores how Genetic Algorithms and Neural Networks models can cooperate to build a system apt to adapt and learn.
Strang, Gilbert, Linear Algebra and Its Applications, San-Diego: Harcout-Brace, Jovanovich, Publishers, 1988. 1-75, 93-126
This book explores the main concepts of linear algebra in a natural order starting with Gaussian Elimination and introducing then the concepts of Matrix, Vector Space and so on.