Solving the lawn mower problem with kaizen programming and lambda-linear genetic programming for module acquisition
Data
2016
Autores
dal Piccol Sotto, Leo Francoso 
de Melo, Vinicius Veloso
Tipo
Trabalho apresentado em evento
Título da Revista
ISSN da Revista
Título de Volume
Resumo
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.
Descrição
Citação
Proceedings Of The 2016 Genetic And Evolutionary Computation Conference (GECCO'16 Companion). New york, p. 113-114, 2016.