Solving the lawn mower problem with kaizen programming and lambda-linear genetic programming for module acquisition
dal Piccol Sotto, Leo Francoso [UNIFESP]
de Melo, Vinicius Veloso [UNIFESP]
TypeTrabalho apresentado em evento
Is part ofProceedings Of The 2016 Genetic And Evolutionary Computation Conference (GECCO'16 Companion)
MetadataShow full item record
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.
CitationProceedings Of The 2016 Genetic And Evolutionary Computation Conference (GECCO'16 Companion). New york, p. 113-114, 2016.
Collaborative Problem Solving