Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The royal tree problem, a benchmark for single and multiple population genetic programming
Advances in genetic programming
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
RoboCup-98: Robot Soccer World Cup II
Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Applications of recursive operators to randomness and complexity
Applications of recursive operators to randomness and complexity
Coding and Information Theory
Using genetic programming to approximate maximum clique
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
ORDERTREE: a new test problem for genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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This report represents an initial investigation into the use of genetic programming to solve the N-prisoners puzzle. The puzzle has generated a certain level of interest among the mathematical community. We believe that this puzzle presents a significant challenge to the field of evolutionary computation and to genetic programming in particular. The overall aim is to generate a solution that encodes complex decision making. Our initial results demonstrate that genetic programming can evolve good solutions. We compare these results to engineered solutions and discuss some of the implications. One of the consequences of this study is that it has highlighted a number of research issues and directions and challenges for the evolutionary computation community. We conclude the article by presenting some of these directions which range over several areas of evolutionary computation, including multi-objective fitness, coevolution and cooperation, and problem representations.