Hybrid coevolutionary programming for Nash equilibrium search in games with local optima
The conventional local optimization path and coevolutionary processes are studied when "local Nash equilibrium (NE) traps" exist. Conventional NE search algorithms in games with local optima can misidentify NE by following a local optimization path. We ...
Learning with case-injected genetic algorithms
This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets ...
A GA-based design space exploration framework for parameterized system-on-a-chip platforms
The constant increase in levels of integration and reduction in the time-to-market has led to the definition of new methodologies, which lay emphasis on reuse. One emerging approach in this context is platform-based design. The basic idea is to avoid ...
On-line genetic design of anti-windup unstructured controllers for electric drives with variable load
In this paper, we describe an evolutionary design procedure for discrete-time anti-windup controller for electrical drives. Using a genetic algorithm devised to test and compare controllers of different orders, we search for the discrete anti-windup ...
Hybrid Taguchi-genetic algorithm for global numerical optimization
In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is proposed to solve global numerical optimization problems with continuous variables. The HTGA combines the traditional genetic algorithm (TGA), which has a powerful global exploration capability,...
An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach
Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary ...
Statistical exploratory analysis of genetic algorithms
Genetic algorithms have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters, and how ...