Verification and Extension of the Theory of Global-Local Hybrids
Sinha, A., Goldberg, D.E. (2001)TR No.: 2001010 | Download PDF | Download PS
Abstract:
This work is an extension of the framework for optimizing global-local hybrids. The existing theory idealizes the search problem as a search by a global searcher for acceptable targets or for basins of attractions which lead to acceptable target by invoking a local searcher. The two key parameters of this theory are—the probabilities […]
Posted: February 8th, 2001 under Genetic algorithms. Comments: none
Modeling of evolution of signaling networks in living cells by evolutionary computation
Kosorukoff, A., Mittenthal, J., Goldberg, D.E. (2001)TR No.: 2001006 | Download PDF | Download PS
Abstract:
This work is an attempt to model the evolution of signaling pathways in living cells
with evolutionary computation under different selection criteria and to compare
pathways evolved in such a way with those we encounter in nature.
It proposes 5-level evolutionary procedure for this purpose, that can be thought as
several co-operating genetic algorithms working in parallel on different […]
Posted: February 8th, 2001 under Genetic algorithms. Comments: none
An Implementation of the Anticipatory Classifier System ACS2 in C++
Butz, M.V. (2001)TR No.: 2001026 | Download PDF | Download PS
Abstract:
A documentation of a C++ implementation of ACS2, the current state-of-the-art of the anticipatory learning classifier system ACS, is provided. The documentation explains how to get started with the code. A detailed overview of the structure of the code and of all possible parameter manipulations are given. Input and Output interfaces are revealed. Finally, the […]
Posted: February 4th, 2001 under Genetic algorithms. Comments: none
Human Based Genetic Algorithm
Kosorukoff, A. (2001)TR No.: 2001004 | Download PDF | Download PS
Abstract:
In this paper, a new class of genetic algorithms (GA) is presented. It is based on the
idea of outsourcing, which is a popular trend in business today. In human based genetic
algorithm (HBGA), all primary genetic operators are outsourced, i.e. delegated to outside
human agents. The totally outsourced genetic algorithm uses both human evaluation and
human ability of […]
Posted: February 4th, 2001 under Genetic algorithms. Comments: none
Genetic Algorithms for Social Innovation and Creativity
Kosorukoff, A., Goldberg, D.E. (2001)TR No.: 2001005 | Download PDF | Download PS
Abstract:
Since their invention, genetic algorithms have been used primarily for solving problems
in different areas of engineering and technology. In most of these areas genetic
algorithms were applied successfully and shifted the frontier between what is possible
and what is impossible, solving problems that were even hard to approach with
conventional deterministic methods.
Recent social applications of genetic algorithms challenge […]
Posted: February 4th, 2001 under Genetic algorithms. Comments: none
Anticipations, Anticipatory Classifier Systems, and Genetic Generalization. A Diploma Thesis from the University of Wuerzburg, Germany.
Butz, M.V. (2001)TR No.: 2001025 | Download PDF | Download PS
Abstract:
Related PostsThe Anticipatory Classifier System and Genetic GeneralizationIntroducing a Genetic Generalization Pressure to the Anticipatory Classifier System Part 2: Performance AnalysisInvestigating Generalization in the Anticipatory Classifier System
Posted: January 24th, 2001 under Genetic algorithms. Comments: none
Evolutionary Algorithm Using Marginal Histogram Models in Continuous Domain
Tsutsui, S., Pelikan, M., Goldberg, D.E. (2001)TR No.: 2001019 | Download PDF | Download PS
Abstract:
Recently, there has been a growing interest in developing
evolutionary algorithms based on probabilistic modeling. In this scheme, the
offspring population is generated according to the estimated probability
density model of the parents instead of using recombination and mutation
operators. In this paper, we propose an evolutionary algorithm using a
marginal histogram to model the parent population in […]
Posted: January 24th, 2001 under Genetic algorithms. Comments: none
How XCS Evolves Accurate Classifiers
Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W. (2001)TR No.: 2001008 | Download PDF | Download PS
Abstract:
Due to the accuracy based fitness approach, the ultimate goal for XCS is the
evolution of a compact, complete, and accurate payoff mapping of an
environment. This paper investigates what causes the XCS classifier system to
evolve accurate classifiers. The investigation leads to two challenges
for XCS, the covering challenge and the schema challenge. Both
challenges are revealed theoretically and
experimentally. […]
Posted: January 24th, 2001 under Genetic algorithms. Comments: none
Efficient Evaluation Genetic Algorithms under Integrated Fitness Functions
Albert, L.A., Goldberg, D.E. (2001)TR No.: 2001024 | Download PDF | Download PS
Abstract:
This paper introduces a framework to describe fitness evaluation error of genetic algorithms (GAs) in which some of the error is due to bias. This framework describes the tradeoffs between accuracy and speed of evaluations and is used to model how computation time can be used efficiently. In particular, fitness functions whose cost […]
Posted: January 20th, 2001 under Genetic algorithms. Comments: none
Tackling Multimodal Problems in Hybrid Genetic Algorithms
Parthasarathy, P.V., Goldberg, D.E., Burns, S.A. (2001)TR No.: 2001012 | Download PDF | Download PS
Abstract:
A method is proposed to address the issue of multimodality while using
hybrid genetic algorithms (GAs). The hybrid GA framework that is used is
one in which a local searcher is employed during a Baldwinian fitness
evaluation. The proposed method, besides offering capability of handling
multimodality, also comes with an added bonus of facilitating evaluation
relaxation. The method has been […]
Posted: January 20th, 2001 under Genetic algorithms. Comments: none