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@PhdThesis{500000550739, author = "Makoto Tanji", title = "Program Evolution using Nonparametric Probabilistic Model", school = "The University of Tokyo", year = "2011", address = "Japan", keywords = "genetic algorithms, genetic programming", URL = "http://ci.nii.ac.jp/naid/500000550739", URL = "http://iss.ndl.go.jp/books/R100000002-I023389085-00", abstract = "This paper describes research results of Genetic Programming (GP) which uses nonparametric probabilistic model. Genetic Programming, a method of Evolutionary Computation which uses mechanism of biological evolution for optimization, can generate programs itself and functions by using tree structure data. It can be applied to complex problems which are difficult to solve by human such as the robot routing problem and the regression problem, etc. Conventional Evolutionary Computation methods, GP and GA (Genetic Algorithm), use crossover and mutation which have been inspired by sexual multiplication. In recent years, new approaches called EDA (Estimation of Distribution Algorithm) and PMBGA (Probabilistic Model Building Genetic Algorithm) which uses a probabilistic model from promising have shown promise. EDA and PMBGA can deal with the dependencies of variables and can solve complex problems in GA. Especially, it is widely accepted that EDA and PMBGA are well suited to problems which have dependencies between the variables. In the past decade, EDA-GP and PMBGP (Probabilistic Model Building GP) have been studied. EDA-GP uses PPT (Probabilistic Prototype Tree) or stochastic grammar as probabilistic model. However it is difficult to represent dependencies of solutions because of a variable length tree structure. Furthermore, the number of parameters and the number of promising solutions increase rapidly. As a result, the computational cost, CPU time and memory, will be high. In this research, a comparative experiment of EDA-GP using PPT model is reported. I propose two methods of GP to overcome the problems above: PORTS (Program Optimization by Random Tree Sampling) and PERCE (Program Evolution using Related Clique Extraction). These methods do not need to calculate and store parameters of a probabilistic model. Instead of storing them, PORTS and PERCE use current solutions directly to create new solutions. PORTS creates a new solution by sampling and concatenation of fragment of promising trees. The size of the fragments follows a geometric distribution to maintain the diversity of a population. Experimental result shows that PORTS solves GP-easy problems very fast than the conventional GP. PERCE uses a set of sub graphs, called ''related cliques'', that contains a number of PPT nodes relating each other. It does not require explicit calculation and storing of the parameters. Experimental result shows that PERCE solves GP-hard problems such as deceptive problem and multimodal problem. I summarize this research and discuss how to use GP for practical problems. Finally, further topics to be researched are presented and discussed.", }

Genetic Programming entries for Makoto Tanji