Independent component analysis: algorithms and applications
Neural Networks
Approximation algorithms for grammar-based compression
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Effective Fitness as an Alternative Paradigm for Evolutionary Computation I: General Formalism
Genetic Programming and Evolvable Machines
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Redundant representations in evolutionary computation
Evolutionary Computation
Theme preservation and the evolution of representation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Factorial representations to generate arbitrary search distributions
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Demonstrating the evolution of complex genetic representations: an evolution of artificial plants
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Representation development from pareto-coevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Minimum description length induction, Bayesianism, and Kolmogorov complexity
IEEE Transactions on Information Theory
Information geometry on hierarchy of probability distributions
IEEE Transactions on Information Theory
Selecting for evolvable representations
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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The choice of representation crucially determines the capability of search processes to find complex solutions in which many variables interact. The question is how good representations can be found and how they can be adapted online to account for what can be learned about the structure of the problem from previous samples. We address these questions in a scenario that we term indirect Estimation-of-Distribution: We consider a decorrelated search distribution (mutational variability) on a variable length genotype space. A one-to-one encoding onto the phenotype space then needs to induce an adapted phenotypic search distribution incorporating the dependencies between phenotypic variables that have been observed successful previously. Formalizing this in the framework of Estimation-of-Distribution Algorithms, an adapted phenotypic search distribution can be characterized as minimizing the Kullback-Leibler divergence (KLD) to a population of previously selected samples (parents). The paper derives a relation between this KLD and the description length of the encoding, stating that compact representations provide a way to minimize the divergence. A proposed class of Compression Evolutionary Algorithms and experiments with an grammar-based compression scheme illustrate the new concept.