Computer-Aided Peptide Evolution for Virtual Drug Design
Belda, I., Llorà, X., Martinell, M., Tarragó, T., Giralt, E. (2004)TR No.: 2004015 | Download PDF | Download PS
Abstract:
One of the goals of computational chemistry is the automated
de novo design of bioactive molecules. Despite significant
progress in computational approaches to ligand design and
efficient evaluation of binding energy, novel procedures for
ligand design are required. Evolutionary computation provides a
new approach to this design issue. A reliable framework for
obtaining ligands via evolutionary algorithms has been
implemented. It provides […]
Posted: March 16th, 2004 under Genetic algorithms. Comments: none
Mixed Decision Trees: Minimizing Knowledge Representation Bias in LCS
Llorà, X., Wilson, S. W. (2004)TR No.: 2004014 | Download PDF | Download PS
Abstract:
Learning classifier systems tend to inherit—a priori—a given
knowledge representation language for expressing the concepts to learn.
Hence, even before getting started, this choice biases what can be learned,
becoming critical for some real-world applications like data mining.
However,
such bias may be minimized by hybridizing different knowledge
representations
via evolutionary mixing. This paper presents a first attempt to produce
an evolutionary framework […]
Posted: March 12th, 2004 under Genetic algorithms. Comments: none
Calculating Efficient Team Size: Balancing Deciding and Doing as an Elementary Optimization Problem
Goldberg, D. E., Yassine, A., Yu, T.-L. (2004)TR No.: 2004013 | Download PDF | Download PS
Abstract:
This paper builds elementary models of efficient team size by balancing costs of deciding and doing that often exists in team settings. A simple model assuming linear decision time and inversely linear execution time is first constructed and optimized. That model is augmented by a concern for solution
quality using simple probability calculations. Thereafter, […]
Posted: March 8th, 2004 under Genetic algorithms. Comments: none
Enhanced Innovation: A Fusion of Chance Discovery and Evolutionary Computation to Foster Creative Processes and Decision Making
Llorà, X., Ohnishi, K., Chen, Y.-P., Goldberg, D. E., Welge, M. E. (2004)TR No.: 2004012 | Download PDF | Download PS
Abstract:
Human-based genetic algorithms are powerful tools for organizational
modeling. If we enhance them using chance discovery techniques, we obtain
an innovative approach for computer-supported collaborative work. Moreover, such a user-centered approach fuses human and computer partners in a
natural way. This paper presents a first test, as well as analyzes the obtained results, of real human and computer […]
Posted: March 4th, 2004 under Genetic algorithms. Comments: none
PAC Learning in XCS
Butz, M. V., Goldberg, D. E., Lanzi, P.-L. (2004)TR No.: 2004011 | Download PDF | Download PS
Abstract:
Although Learning Classifier Systems date back more than twenty years ago,
theory regarding issues like convergence or computational effort remained sparse. This paper establishes a PAC learning bound for the accuracy-based classifier system XCS. XCS is a flexible genetic-based learning mechanism applicable in classification problems, reinforcement learning problems, as well as different types of representations. Using […]
Posted: February 24th, 2004 under Genetic algorithms. Comments: none
Efficiency Enhancement of Genetic Algorithms via Building-Block-Wise Fitness Estimation
Sastry, K., Pelikan, M., Goldberg, D. E. (2004)TR No.: 2004010 | Download PDF | Download PS
Abstract:
This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function evaluations. The effect of fitness inheritance on the convergence time […]
Posted: February 20th, 2004 under Genetic algorithms. Comments: none
Fitness inheritance in the Bayesian optimization algorithm
Pelikan, M., Sastry, K. (2004)TR No.: 2004009 | Download PDF | Download PS
Abstract:
This paper describes how fitness inheritance can be used to estimate
fitness for a proportion of newly sampled candidate solutions in the
Bayesian optimization algorithm (BOA). The goal of estimating fitness for
some candidate solutions is to reduce the number of fitness evaluations
for problems where fitness evaluation is expensive. Bayesian networks used
in BOA to model promising solutions and […]
Posted: February 16th, 2004 under Genetic algorithms. Comments: none
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
Pedersen, G., Goldberg, D. E. (2004)TR No.: 2004008 | Download PDF | Download PS
Abstract:
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can be obtained for badly scaled objective functions. This is especially a […]
Posted: February 12th, 2004 under Genetic algorithms. Comments: none
Inducing Sequentiality Using Grammatical Genetic Codes
Ohnishi, K., Sastry, K., Chen, Y.-P., Goldberg, D. E. (2004)TR No.: 2004007 | Download PDF | Download PS
Abstract:
This paper studies the inducement of sequentiality in genetic algorithms (GAs) for uniformly-scaled problems. Sequentiality is a phenomenon in which sub-solutions converge sequentially in time in contrast to uniform convergence observed for uniformly-scaled problems. This study uses three different grammatical genetic codes to induce sequentiality. Genotypic genes in the grammatical codes are interpreted as phenotypes […]
Posted: February 8th, 2004 under Genetic algorithms. Comments: none
Quality and Efficiency of Model Building for Genetic Algorithms
Yu, T.-L., Goldberg, D. E. (2004)TR No.: 2004004 | Download PDF | Download PS
Abstract:
This paper investigates the linkage model building for genetic
algorithms. By assuming a given quality of the linkage model, a
analytical model of time to convergence is derived. Given the
computational cost of building the linkage model, an estimated
total computational time is obtained by using the derived
time-to-convergence model. The models are empirically verified.
The results can be potentially used […]
Posted: February 4th, 2004 under Genetic algorithms. Comments: none