Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An approach to a problem in network design using genetic algorithms
An approach to a problem in network design using genetic algorithms
The royal tree problem, a benchmark for single and multiple population genetic programming
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Causality in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Neutrality: a necessity for self-adaptation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A description of holland's royal road function
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Maximum homologous crossover for linear genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Helper-objective optimization strategies for the Job-Shop Scheduling Problem
Applied Soft Computing
A framework for multi-model EDAs with model recombination
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Analysis of a triploid genetic algorithm over deceptive and epistatic landscapes
ACM SIGAPP Applied Computing Review
A multimodal problem for competitive coevolution
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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The fitness landscape of a problem is the relation between the solution candidates and their reproduction probability. In order to understand optimization problems, it is essential to also understand the features of fitness landscapes and their interaction. In this paper we introduce a model problem that allows us to investigate many characteristics of fitness landscapes. Specifically noise, affinity for overfitting, neutrality, epistasis, multi-objectivity, and ruggedness can be independently added, removed, and fine-tuned. With this model, we contribute a useful tool for assessing optimization algorithms and parameter settings.