Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints
Communications of the ACM - Electronic supplement to the December issue
Software—Practice & Experience
Bargaining theory with applications
Bargaining theory with applications
Foundations of genetic programming
Foundations of genetic programming
Data Mining and Knowledge Discovery
Abstracting soft constraints: framework, properties, examples
Artificial Intelligence
Inductive Genetic Programming with Decision Trees
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
GA-easy and GA-hard Constraint Satisfaction Problems
Constraint Processing, Selected Papers
Arc consistency for soft constraints
Artificial Intelligence
EDDIE-automation, a decision support tool for financial forecasting
Decision Support Systems - Special issue: Data mining for financial decision making
Handbook of Research on Nature-inspired Computing for Economics and Management
Handbook of Research on Nature-inspired Computing for Economics and Management
Journal of Artificial Intelligence Research
Evolutionary computation and games
IEEE Computational Intelligence Magazine
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Comparing evolutionary algorithms on binary constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Constraint-directed search in computational finance and economics
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Digital IIR filter design using multi-objective optimization evolutionary algorithm
Applied Soft Computing
Bargaining strategies designed by evolutionary algorithms
Applied Soft Computing
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This paper presents an evolutionary algorithms based constrain-guided method (CGM) that is capable of handling both hard and soft constraints in optimization problems. While searching for constraint-satisfied solutions, the method differentiates candidate solutions by assigning them with different fitness values, enabling favorite solutions to be distinguished more likely and more effectively from unfavored ones. We illustrate the use of CGM in solving two economic problems with optimization involved: (1) searching equilibriums for bargaining problems; (2) reducing the rate of failure in financial prediction problems. The efficacy of the proposed CGM is analyzed and compared with some other computational techniques, including a repair method and a penalty method for the problem (1), a linear classifier and three neural networks for the problem (2), respectively. Our studies here suggest that the evolutionary algorithms based CGM compares favorably against those computational approaches.