%0 Book Section %1 Poli:1999:nio %A Poli, Riccardo %B New Ideas in Optimization %C Maidenhead, Berkshire, England %D 1999 %E Corne, David %E Dorigo, Marco %E Glover, Fred %I McGraw-Hill %K PDGP algorithms, genetic programming, %P 403--431 %T Parallel Distributed Genetic Programming %U http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-NIO-1999-PDGP.pdf %X This chapter describes Parallel Distributed Genetic Programming (PDGP), a form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an efficient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. In the simplest form of PDGP links are directed and unlabelled, in which case PDGP can be considered a generalisation of standard GP. However, more complex representations can be used, which allow the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, recurrent transition networks, finite state automata, etc. In PDGP, programs are manipulated by special crossover and mutation operators which guarantee the syntactic correctness of the offspring. For this reason PDGP search is very efficient. PDGP programs can be execut... %& 27 %@ 0-07-709506-5

@incollection{Poli:1999:nio, abstract = {This chapter describes Parallel Distributed Genetic Programming (PDGP), a form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an efficient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. In the simplest form of PDGP links are directed and unlabelled, in which case PDGP can be considered a generalisation of standard GP. However, more complex representations can be used, which allow the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, recurrent transition networks, finite state automata, etc. In PDGP, programs are manipulated by special crossover and mutation operators which guarantee the syntactic correctness of the offspring. For this reason PDGP search is very efficient. PDGP programs can be execut...}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Maidenhead, Berkshire, England}, author = {Poli, Riccardo}, biburl = {https://www.bibsonomy.org/bibtex/2ca271303b36693938a5637e56db02e50/brazovayeye}, booktitle = {New Ideas in Optimization}, chapter = 27, editor = {Corne, David and Dorigo, Marco and Glover, Fred}, interhash = {4bcac060faf32050ff612c2729d03bda}, intrahash = {ca271303b36693938a5637e56db02e50}, isbn = {0-07-709506-5}, keywords = {PDGP algorithms, genetic programming,}, notes = {This is the most complete account of PDGP and its performance so far. E.g. a symbolic regression problem x^6-2*x^4+x^2 in which PDGP does 16 times better than std GP and 13 times better than GP with ADFs. XOR, lawnmower, sextic polynomial, encoder-decoder FSA induction, NLP,}, pages = {403--431}, publisher = {McGraw-Hill}, series = {Advanced Topics in Computer Science}, size = {29 pages}, timestamp = {2008-06-19T17:49:43.000+0200}, title = {Parallel Distributed Genetic Programming}, url = {http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-NIO-1999-PDGP.pdf}, year = 1999 }