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4,002
Genetic-Based Search for Error-Correcting Graph Isomorphism
- IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics
, 1997
"... Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorith ..."
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Cited by 24 (0 self)
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Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic
A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques
- Knowledge and Information Systems
, 1998
"... . This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search cap ..."
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Cited by 292 (22 self)
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: multiobjective optimization, multicriteria optimization, vector optimization, genetic algorithms, evolutionary algorithms, artificial intelligence. 1 Introduction Since the pioneer work of Rosenberg in the late 60s regarding the possibility of using genetic-based search to deal with multiple objectives
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
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Cited by 356 (12 self)
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the framework of competitive learning. This new perspective reveals a number of different possibilities for performance improvements. This paper explores population-based incremental learning (PBIL), a method of combining the mechanisms of a generational genetic algorithm with simple competitive learning
Genetic-Based Planning with Recursive Subgoals
"... Abstract—In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a genetic-based planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a goa ..."
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Abstract—In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a genetic-based planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a
A coupled random search-shape grammar algorithm for the control of reconfigurable pixel microstrip antennas
- IJASM
"... Abstract—Algorithms are necessary to reconfigure pixel antennas in real time. These algorithms must carry out an efficient search to yield the electrical specifications demanded by radio transceivers. To address this problem, an algorithm that combines a random search method with a shape gram-mar mo ..."
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Cited by 1 (0 self)
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-mar model is proposed. The performance of the algorithm is compared to a Genetic Algorithm with and without a pruning mechanism. Results demonstrate that the algorithm goes through a relatively very small number of iterations to converge to the antenna specifications and consequently outperforms the genetic-based
A Survey of Optimization by Building and Using Probabilistic Models
- COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
, 1999
"... This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field of ge ..."
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Cited by 339 (90 self)
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This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the field
Geniminer: Web Mining With A Genetic-Based
, 2002
"... We present in this paper a genetic search strategy for a search engine. We begin by showing that important relations exist between Web statistical studies, search engines, and standard techniques in optimization: the web is a graph which can be searched for relevant information with an evaluation fu ..."
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function and with operators based on standard search engines or local exploration. It is then straightforward to define an evaluation timerion that is a mathematical formulation of the user request and to define a steady state genetic algorithm that evolves a population of pages with binary tournament
MACHINE LEARNING USING A GENETIC-BASED APPROACH.
"... ABSRACT: A Holland learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications. An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned t ..."
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ABSRACT: A Holland learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications. An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned
An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends
- Proceedings of the Congress on Evolutionary Computation
, 1999
"... This paper reviews some of the most popular evolutionary multiobjective optimization techniques currently reported in the literature, indicating some of their main applications, their advantages, disadvantages, and degree of aplicability. Finally, some of the most promising areas of future research ..."
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Cited by 89 (0 self)
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are briefly discussed. 1 Introduction Since the pioneering work of Rosenberg in the late 1960s regarding the possibility of using genetic-based search to deal with multiple objectives [1], this new area of research (now called Evolutionary Multi-Objective Optimization, or EMOO for short) has grown
Approximating the nondominated front using the Pareto Archived Evolution Strategy
- EVOLUTIONARY COMPUTATION
, 2000
"... We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its ..."
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Cited by 321 (19 self)
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involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1 + λ) and (μ | λ) variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants
Results 1 - 10
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4,002
