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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)
—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)
We give a new interpretation to the concept of "landscape". This allows us to develop a new theoretical model to study search algorithms. Particularly, we are able to quantify the <i>amount</i> and <i>quality</i> of "information" embedded in a landscape and to predict the performance of a search algorithm over it. We conclude presenting empirical results(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)
We review the main results obtained in the theory of schemata in genetic programming (GP), emphasizing their strengths and weaknesses. Then we propose a new, simpler definition of the concept of schema for GP, which is closer to the original concept of schema in genetic algorithms (GAs). Along with a new form of crossover, one-point crossover, and point(More)