Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Two self-adaptive crossover operators for genetic programming
Advances in genetic programming
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Evolving evolutionary algorithms using evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Evolutionary design of Evolutionary Algorithms
Genetic Programming and Evolvable Machines
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Automatically designing selection heuristics
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolving evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The automatic generation of mutation operators for genetic algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifically tailored to that class to significantly outperform more general purpose problem solvers. However, the fields that encompass BBSAs, including Evolutionary Computing, are mostly focused on improving algorithm performance over increasingly diversified problem classes. By definition, the payoff for designing a high quality general purpose solver is far larger in terms of the number of problems it can address, than a specialized BBSA. This paper introduces a novel approach to creating tailored BBSAs through automated design employing genetic programming. An experiment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs including canonical evolutionary algorithms.