Tomasz Pawlak

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This study presents an extensive account of Locally Geometric Semantic Crossover (LGX), a semantically-aware recombination operator for genetic programming (GP). LGX is designed to exploit the semantic properties of programs and subprograms, in particular the geometry of semantic space that results from distance-based fitness functions used predominantly in(More)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract—In genetic programming, a search algorithm is expected to produce a program that achieves the desired final computation state (desired output). To reach that state, an executing program(More)
We propose a novel crossover operator for tree-based genetic programming, that produces approximately geometric offspring. We empirically analyze certain aspects of geometry of crossover operators and verify performance of the new operator on both, training and test fitness cases coming from set of symbolic regression benchmarks. The operator shows superior(More)
This paper provides a structured, unified, formal and empirical perspective on all geometric semantic crossover operators proposed so far, including the exact geometric crossover by Moraglio, Krawiec, and Johnson, as well as the approximately geometric operators. We start with presenting the theory of geometric semantic genetic programming, and discuss the(More)
Metrics are essential for geometric semantic genetic programming. On one hand, they structure the semantic space and govern the behavior of geometric search operators; on the other, they determine how fitness is calculated. The interactions between these two types of metrics are an important aspect that to date was largely neglected. In this paper, we(More)