Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Foundations of genetic programming
Foundations of genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Effective Fitness as an Alternative Paradigm for Evolutionary Computation I: General Formalism
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Using Schema Theory To Explore Interactions Of Multiple Operators
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
General schema theory for genetic programming with subtree-swapping crossover: part I
Evolutionary Computation
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
Evolutionary Computation
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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We present a theoretical analysis of Watson's Hierarchical-if-and-only-if (HIFF) problem using a variety of tools. These include schema theory and course graining, the concept of effective fitness, and statistical analysis. We first review the use of Stephen's exact schema equations and schema basis to compute the changes in population distributions over time. We then use the tools described above to solve for the limit distributions of the 2 and 4-bit HIFF problems, and show that these limit distributions are essentially one-dimensional. We also show that a combination of fitness and the number of break points (a rough measure of distance in crossover space) in a string can be used to almost completely explain the limit distribution in the 4-bit HIFF problem.