Fitness Inheritance in Multi-Objective Optimization
Chen, J.-H., Goldberg, D. E., Hoand, S.-Y., Sastry, K. (2002)TR No.: 2002017 | Download PDF | Download PS
Abstract:
In real-world multi-objective problems, the evaluation of objective functions usually requires a large amount of computation time. Moreover, due to the curse of dimensionality, solving multiobjective problems often requires much longer computation time than solving single-objective problems. Therefore, it is essential to develop e?ciency enhancement techniques for solving multi-objective problems. This paper investigates fitness inheritance […]
Posted: March 24th, 2002 under Genetic algorithms. Comments: none
Accuracy, Parsimony, and Generality in Evolutionary LearningSystems via Multiobjective Selection
Llorà , X., Goldberg, D. E., Traus, I., Bernadó, E. (2002)TR No.: 2002016 | Download PDF | Download PS
Abstract:
Learning systems (also known as Pittsburgh learning classifier systems) need to balance accuracy and parsimony for evolving high quality general hypotheses. The evolutionary learning process used in learning systems is based on using a set of training instances that sample the target concept to be learned. Thus, the the learning process may overfit the learned […]
Posted: March 20th, 2002 under Genetic algorithms. Comments: none
Minimal Achievable Error in the LED problem
Llorà, X., Goldberg, D. E. (2002)TR No.: 2002015 | Download PDF | Download PS
Abstract:
This paper presents a theoretical model to predict the minimal achievable error, given a noise ratio, in the LED data set problem. The motivation for developing this theoretical model is to understand and explain some of the results that different systems achieve when they solve the LED problem. Moreover, given a new learning algorithm that […]
Posted: March 16th, 2002 under Genetic algorithms. Comments: none
The Influence of Binary Representations of Integers on thePerformance of Selectorecombinative Genetic Algorithms
Rothlauf, F. (2002)TR No.: 2002014 | Download PDF | Download PS
Abstract:
When using representations for genetic algorithms (GAs) every optimization problem can be separated into a genotype-phenotype and a phenotype-fitness mapping. The genotype-phenotype mapping is the used representation and the phenotype-fitness mapping is the problem that should be solved.
This paper investigates how the use of different binary representations of integers influences the performance of selectorecombinative GAs […]
Posted: March 12th, 2002 under Genetic algorithms. Comments: none
Theory of Generalization and Learning in XCS
Butz, M., Kovacs, T., Lanzi, P. L., Wilson, S. W. (2002)TR No.: 2002011 | Download PDF | Download PS
Abstract:
The XCS classifier system evolves accurate, maximally general solutions to a wide variety of machine learning, data, and robotics problems, but a theoretical basis for these properties has not been presented. This paper develops and tests a model for the generalization pressure, and establishes conditions for the existence of effective fitness or accuracy pressure in […]
Posted: March 8th, 2002 under Genetic algorithms. Comments: none
How Well Does a Single-Point Crossover Mixing Building Blocks with Tight Linkage?
Sastry, K., Goldberg, D. E. (2002)TR No.: 2002013 | Download PDF | Download PS
Abstract:
Ensuring building-block (BB) mixing is critical to the success of genetic and evolutionary algorithms. This study develops facetwise models to predict the BB mixing time and the population sizing dictated by BB mixing for single-point crossover. Empirical results are used to validate these models. The population-sizing model suggests that for moderate-to-large problems, BB mixing—instead of […]
Posted: March 4th, 2002 under Genetic algorithms. Comments: none
Analysis of Mixing in Genetic Algorithms: A Survey
Sastry, K., Goldberg, D. E. (2002)TR No.: 2002012 | Download PDF | Download PS
Abstract:
Ensuring building-block (BB) mixing is critical to the success of genetic and evolutionary algorithms. There has been a growing interest in analyzing and understanding BB mixing and it is necessary to organize and categorize representative literature. This paper presents an exhaustive survey of studies on one or more aspects of mixing. In doing so, a […]
Posted: February 24th, 2002 under Genetic algorithms. Comments: none
Genetic Algorithms at the University of Illinois Fall 2001
Goldberg, D. E. (ed.) (2002)TR No.: 2002010 | Download PDF | Download PS
Abstract:
Related PostsGenetic Algorithms at the University of Illinois Fall 1999Genetic Algorithms at the University of Illinois Fall 2000Genetic Algorithms at the University of Illinois Fall 2002
Posted: February 20th, 2002 under Genetic algorithms. Comments: none
Multi-objective Bayesian Optimization Algorithm
Khan, N., Goldberg, D.E., Pelikan, M. (2002)TR No.: 2002009 | Download PDF | Download PS
Abstract:
This paper proposes a competent multi-objective genetic algorithm
called the multi-objective Bayesian optimization algorithm
(mBOA). mBOA incorporates the selection method of the non-dominated
sorting genetic algorithm-II (NSGA-II) into the Bayesian optimization
algorithm (BOA). The proposed algorithm has been tested on an array of
test functions which incorporate deception and loose-linkage and the
results are compared to those of NSGA-II. Results indicate […]
Posted: February 16th, 2002 under Genetic algorithms. Comments: none
XCS with Average Reward Criterion in Multi-Step Environment.
Tharakunnel, K., Goldberg, D.E. (2002)TR No.: 2002008 | Download PDF | Download PS
Abstract:
In multi-step environment, the XCS prediction parameter is an estimate of the discounted sum of
successive rewards. Thus, XCS isdesigned to address sequential (multi-step) decision problems
with the objective of maximization of the total discounted rewards. However, there are many
real-world sequential decision problems where the preferred objective is maximization of the average of successive rewards. In […]
Posted: February 12th, 2002 under Genetic algorithms. Comments: none