Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems

2010 ◽  
pp. 17-33 ◽  
Author(s):  
Lee Spector
Keyword(s):  
Problem Solving ◽  
Genetic Programming ◽  
Programming Systems

scholarly journals Exploring genetic programming systems with MAP-Elites

2018 ◽  
Author(s):  
Emily Dolson ◽  
Alexander Lalejini ◽  
Charles Ofria
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Pareto Front ◽  
Underlying Structure ◽  
Phenotypic Traits ◽  
Computation Technique ◽  
Phenotype Space ◽  
Programming Systems ◽  
Insight Into ◽  
Connection Cost

MAP-Elites is an evolutionary computation technique that has proven valuable for exploring and illuminating the genotype-phenotype space of a computational problem. In MAP-Elites, a population is structured based on phenotypic traits of prospective solutions; each cell represents a distinct combination of traits and maintains only the most fit organism found with those traits. The resulting map of trait combinations allows the user to develop a better understanding of how each trait relates to fitness and how traits interact. While MAP-Elites has not been demonstrated to be competitive for identifying the optimal Pareto front, the insights it provides do allow users to better understand the underlying problem. In particular, MAP-Elites has provided insight into the underlying structure of problem representations, such as the value of connection cost or modularity to evolving neural networks. Here, we extend the use of MAP-Elites to examine genetic programming representations, using aspects of program architecture as traits to explore. We demonstrate that MAP-Elites can generate programs with a much wider range of architectures than other evolutionary algorithms do (even those that are highly successful at maintaining diversity), which is not surprising as this is the purpose of MAP-Elites. Ultimately, we propose that MAP-Elites is a useful tool for understanding why genetic programming representations succeed or fail and we suggest that it should be used to choose selection techniques and tune parameters.

Grammar Design for Derivation Tree Based Genetic Programming Systems

2016 ◽  
pp. 199-214 ◽  
Author(s):  
Stefan Forstenlechner ◽  
Miguel Nicolau ◽  
David Fagan ◽  
Michael O’Neill
Keyword(s):  
Genetic Programming ◽  
Derivation Tree ◽  
Programming Systems

Genetic programming III: Darwinian invention and problem solving [Book Review]

1999 ◽  
Vol 3 (3) ◽  
pp. 251-253 ◽  
Author(s):  
J.R. Koza ◽  
F.H. Bennett ◽  
D. Andre ◽  
M.A. Keane
Keyword(s):  
Problem Solving ◽  
Genetic Programming

Evolutionary Algorithms in Problem Solving and Machine Learning

2011 ◽  
pp. 124-137
Author(s):  
Marco Tomassini ◽  
Leonardo Vanneschi
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Problem Solving ◽  
Drug Design ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Portfolio Allocation ◽  
Allocation Problem ◽  
Financial Problem ◽  
Sufficient Detail

In the first part of the chapter, evolutionary algorithms are briefly described, especially genetic algorithms and genetic programming, with sufficient detail so as to prepare the ground for the second part. The latter presents in more detail two specific applications. The first is about an important financial problem: the portfolio allocation problem. The second one deals with a biochemical problem related to drug design and efficacy.

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