Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability
Sastry, K. (2007)TR No.: 2007019 | Download PDF | Download PS
Abstract:
Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent […]
Posted: September 12th, 2007 under Genetic algorithms. Comments: none
Fluctuating Crosstalk, GA Scalability, and the Disruption of Optima
Winward, P. (2007)TR No.: 2007018 | Download PDF | Download PS
Abstract:
The genetic algorithm (GA) is gaining increasing interest in both academia and industry in attempts to solve hard search problems quickly, accurately, and reliably. Various theories of what makes a problem difficult for the GA to solve have been put forward; yet, none of them has been completely confirmed experimentally. This thesis examines […]
Posted: September 12th, 2007 under Genetic algorithms. Comments: none
Single and Multiobjective Genetic Algorithm Toolbox for Matlab in C++
Sastry, K. (2007)TR No.: 2007017 | Download PDF | Download PS
[Download source code]. Part of Materials Computation Center software archive.
Abstract:
This report provides documentation for the general purpose genetic algorithm toolbox for matlab in C++. The fitness function used in the toolbox is written in matlab. The toolbox provides different selection, recombination, mutation, niching, and constraint-handling operators. Problems with single and multiple objectives can be solved […]
Posted: June 26th, 2007 under Genetic algorithms. Comments: none
Single and Multiobjective Genetic Algorithm Toolbox in C++
Sastry, K. (2007)TR No.: 2007016 | Download PDF | Download PS
[Download source code]. Part of Materials Computation Center software archive.
Abstract:
This report provides documentation for the general purpose genetic algorithm toolbox. The toolbox provides different selection, recombination, mutation, niching, and constraint-handling operators. Problems with single and multiple objectives can be solved with the toolbox. Moreover, the toolbox is easily extensible and customizable for incorporating other operators […]
Posted: June 26th, 2007 under Genetic algorithms. Comments: none
Automated Alphabet Reduction with Evolutionary Algorithms for Protein Structure Prediction
Bacardit, J., Stout, M., Hirst, J. D., Sastry, K., Llorà, X., Krasnogor, N. (2007)TR No.: 2007015 | Download PDF | Download PS
Abstract:
This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a protein is
represented. The potential benefits of this procedure are faster and easier learning process as well as the generation of more compact and human-readable classifiers. The dimensionality reduction procedure […]
Posted: March 16th, 2007 under Genetic algorithms. Comments: none
A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms
Chen, Y.-p., Yu, T.-L., Sastry, K., Goldberg, D. E. (2007)TR No.: 2007014 | Download PDF | Download PS
Abstract:
This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of
learning linkage, which is referred to as the relationship between […]
Posted: March 12th, 2007 under Genetic algorithms. Comments: none
Influence of Selection and Replacement Strategies on Linkage Learning in BOA
Lima, C. F., Pelikan, M., Goldberg, D. E., Lobo, F. G., Sastry, K., Hauschild, M. (2007)TR No.: 2007013 | Download PDF | Download PS
Abstract:
The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. This paper studies the influence of different selection and replacement methods on the accuracy of linkage learning in BOA.
Results on concatenated m-k deceptive trap functions show that the model accuracy depends on a large extent on […]
Posted: March 8th, 2007 under Genetic algorithms. Comments: none
A Matrix Approach for Finding Extrema: Problems with Modularity, Hierarchy, and Overlap
Yu, T.-L. (2007)TR No.: 2007012 | Download PDF | Download PS
Abstract:
Unlike most simple textbook examples, the real world is full with complex systems, and researchers in many different fields are often confronted by problems arising from such systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper
decomposition according to the complex […]
Posted: March 4th, 2007 under Genetic algorithms. Comments: none
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning Techniques
Llorà, X., Sastry, K., Yu, T.-L., Goldberg, D. E. (2007)TR No.: 2007011 | Download PDF | Download PS
Abstract:
One benefit of using probabilistic model-building genetic algorithms is the possibility of creating cheap and accurate surrogate models. Learning classifier systems—and genetics-based machine learning in general—can greatly benefit from such surrogates which can replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate […]
Posted: February 24th, 2007 under Genetic algorithms. Comments: none
Substructrual Surrogates for Learning Decomposable Classification Problems: Implementation and First Results
Orriols-Puig, A., Sastry, K., Goldberg, D. E., Bernadó-Mansilla, E. (2007)TR No.: 2007010 | Download PDF | Download PS
Abstract:
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of […]
Posted: February 20th, 2007 under Genetic algorithms. Comments: none