Foundations of genetic programming
Foundations of genetic programming
Some results about the Markov chains associated to GPs and general EAs
Theoretical Computer Science - Foundations of genetic algorithms
Free lunches for function and program induction
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Abstract convex evolutionary search
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Phenotypic diversity in initial genetic programming populations
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Geometric semantic genetic programming
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Geometric Semantic Genetic Programming (GSGP) is a recently introduced form of Genetic Programming (GP), rooted in a geometric theory of representations, that searches directly the semantic space of functions/programs, rather than the space of their syntactic representations (e.g., trees) as in traditional GP. Remarkably, the fitness landscape seen by GSGP is always -- for any domain and for any problem -- unimodal with a linear slope by construction. This has two important consequences: (i) it makes the search for the optimum much easier than for traditional GP; (ii) it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. The runtime analysis of GP has been very hard to tackle, and only simplified forms of GP on specific, unrealistic problems have been studied so far. We present a runtime analysis of GSGP with various types of mutations on the class of all Boolean functions.