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Espresso to the rescue of genetic programming facing exponential complexity

Published:19 July 2022Publication History

ABSTRACT

Fitness computation is a crucial part of any population-based evolutionary strategy. In Genetic Programming (GP) each individual of a population is evaluated by comparing the output they return when fed some input with the expected output. The mapping input/output form the test cases, it describes the "behaviour" an individual should have when presented an instance of a problem. However, there exist situations in which the mapping is given as a function an individual should implement, thus consisting in combinations of several variables of the problem. This paper addresses the issue of efficiently computing the fitness of an individual evaluated on such test sets exponentially large in the number of variables. The inspiration came from digital electronics and more specifically the ESPRESSO-MV algorithm, accepting multi-valued variables. The proposed datastructure represents subsets of the solution space, hence allowing to compute the number of passed test cases as a single operation rather than enumerating all of them. The heuristics used rely on the Unate Recursive Paradigm (URP), some of the proposed algorithms are new while others come directly from ESPRESSO-MV.

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          cover image ACM Conferences
          GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
          July 2022
          2395 pages
          ISBN:9781450392686
          DOI:10.1145/3520304

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          Publication History

          • Published: 19 July 2022

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