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The latest book on Genetic Programming, Poli, Langdon and McPhee's (with contributions from John R. Koza) A Field Guide to Genetic Programming represents an exciting landmark with the authors choosing to make their work freely available by publishing using a form of the Creative Commons License[1]. In so doing they have created a must-read resource which(More)
A Brief Tour of Evolutionary A Brief Tour of Evolutionary Computation Computation z z Evolutionary computation: Evolutionary computation: Machine learning Machine learning optimization and classification paradigms roughly optimization and classification paradigms roughly based on mechanisms of evolution such as biological based on mechanisms of evolution(More)
—Genetic Algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of computation load and memory. Parallel Genetic Algorithms (PGAs) are parallel implementations of GAs which can provide considerable gains in terms of performance and scalability. PGAs(More)
In the last decade and a half, the amount of work on affect in general and emotion in particular has grown, in empirical psychology, cognitive science and AI, both for scientific purposes and for the purpose of designing synthetic characters, e.g. in games and entertainments. Such work understandably starts from concepts of ordinary language (e.g. " emotion(More)
The problem of evolving an artiicial ant to follow the Santa Fe trail is used to demonstrate the well known genetic programming feature of growth in solution length. Known variously as \bloat", \redundancy", \introns", \\uu", \Structural Complexity" with antonyms \parsimony", \Minimum Description Length" (MDL) and \Occam's razor". Comparison with runs with(More)