Riccardo Poli

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ion operator, 48<lb>active code, 102<lb>adaptive market hypothesis, 123<lb>ADATE, 48<lb>ADF<lb>crossover, 49<lb>recursion prevention, 49<lb>troubleshooting, 134<lb>agent<lb>evolutionary, 122<lb>image processing, 122<lb>social simulation, 123<lb>aggregate fitness function, 75–76<lb>analogue circuit evolution, 119<lb>ant colony optimisation (ACO), 74<lb>PEEL,(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 can(More)
This paper describes Parallel Distributed Genetic Programming (PDGP), a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of paral-lelism and an eecient and eeective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links(More)
The problem of evolving an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as “bloat”, “fluff” and increasing “structural complexity”, this is often described in terms of increasing “redundancy” in the code caused by “introns”. Comparison between runs with(More)
The phenomenon of growth in program size in genetic programming populations has been widely reported. In a variety of experiments and static analysis we test the standard protective code explanation and find it to be incomplete. We suggest bloat is primarily due to distribution of fitness in the space of possible programs and because of this, in the absence(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.(More)
The problem of evolving, using mutation, an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as “bloat”, “fluff” and increasing “structural complexity”, this is often described in terms of increasing “redundancy” in the code caused by “introns”. Comparison(More)