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 evolution of evolvability in genetic programming
Advances in genetic programming
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
New methods for competitive coevolution
Evolutionary Computation
Evolving 3d morphology and behavior by competition
Artificial Life
Artificial Life
Shapes in the shadow: evolutionary dynamics of morphogenesis
Artificial Life
Special Purpose Image Convolution with Evolvable Hardware
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Optimization as Side-Effect of Evolving Allelopathic Diversity
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Tournament-Based Competitive Coevolutionary Algorithm
Applied Intelligence
The MaxSolve algorithm for coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Investigating the success of spatial coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ideal Evaluation from Coevolution
Evolutionary Computation
The parallel Nash Memory for asymmetric games
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
The role of speciation in spatial coevolutionary function approximation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A comparison of evaluation methods in coevolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Methods for evolving robust programs
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Predicting solution rank to improve performance
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Coevolution in cartesian genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Spatial co-evolution: quicker, fitter and less bloated
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An investigation of local patterns for estimation of distribution genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Acceleration of evolutionary image filter design using coevolution in cartesian GP
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Better GP benchmarks: community survey results and proposals
Genetic Programming and Evolvable Machines
Controlling bloat through parsimonious elitist replacement and spatial structure
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Program optimisation with dependency injection
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Introducing graphical models to analyze genetic programming dynamics
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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Most evolutionary optimization models incorporate a fitness evaluation that is based on a predefined static set of test cases or problems. In the natural evolutionary process, selection is of course not based on a static fitness evaluation. Organisms do not have to combat every existing disease during their lifespan; organisms of one species may live in different or changing environments; different species coevolve. This leads to the question of how information is integrated over many generations. This study focuses on the effects of different fitness evaluation schemes on the types of genotypes and phenotypes that evolve. The evolutionary target is a simple numerical function. The genetic representation is in the form of a program (i.e., a functional representation, as in genetic programming). Many different programs can code for the same numerical function. In other words, there is a many-to-one mapping between “genotypes” (the programs) and “phenotypes”. We compare fitness evaluation based on a large static set of problems and fitness evaluation based on small coevolving sets of problems. In the latter model very little information is presented to the evolving programs regarding the evolutionary target per evolutionary time step. In other words, the fitness evaluation is very sparse. Nevertheless the model produces correct solutions to the complete evolutionary target in about half of the simulations. The complete evaluation model, on the other hand, does not find correct solutions to the target in any of the simulations. More important, we find that sparse evaluated programs are better generalizable compared to the complete evaluated programs when they are evaluated on a much denser set of problems. In addition, the two evaluation schemes lead to programs that differ with respect to mutational stability; sparse evaluated programs are less stable than complete evaluated programs.