Solving the lawn mower problem with kaizen programming and lambda-linear genetic programming for module acquisition

Solving the lawn mower problem with kaizen programming and lambda-linear genetic programming for module acquisition

Author dal Piccol Sotto, Leo Francoso Autor UNIFESP Google Scholar
de Melo, Vinicius Veloso Autor UNIFESP Google Scholar
Abstract In this work, we have tested a new approach for evolving modular programs: Kaizen Programming (KP) with lambda-Linear Genetic Programming (lambda-LGP) and a heuristic search procedure to solve the well-known Lawn Mower problem. KP is a novel hybrid approach that tries to efficiently combine partial solutions to generate a high-quality complete solution. Being a hybrid, KP may use different types of methods to generate partial solutions, assess their importance to the complete solution, and solve the complete problem. Experiments on the Lawn Mower problem show that the proposed method is effective in finding the expected solution. It is a new alternative for evolving modular programs, but further investigations are necessary to improve its performance.
Keywords Kaizen Programming
Collaborative Problem Solving
Genetic Programming
Modularity
Lawnmower Problem
Language English
Date 2016
Published in Proceedings Of The 2016 Genetic And Evolutionary Computation Conference (GECCO'16 Companion). New york, p. 113-114, 2016.
Publisher Associacao Paulista Medicina
Extent 113-114
Origin http://dx.doi.org/10.1145/2908961.2909007
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000383741800057
URI http://repositorio.unifesp.br/handle/11600/49402

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