Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Practical genetic algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Empirical Modelling of Genetic Algorithms
Evolutionary Computation
Performance Evaluation of Multiagent Genetic Algorithm
Natural Computing: an international journal
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Design and Analysis of Experiments
Design and Analysis of Experiments
Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
Performance evaluation and population reduction for a self adaptive hybrid genetic algorithm (SAHGA)
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
An adaptive resolution hybrid binary-real coded genetic algorithm
Artificial Life and Robotics
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Solving extremely difficult MINLP problems using adaptive resolution Micro-GA with tabu search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
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Real parameter constrained problems are an important class of optimization problems that are encountered frequently in a variety of real world problems. On one hand, Genetic Algorithms (GAs) are an efficient search metaheuristic and a prominent member within the family of Evolutionary Algorithms (EAs), which have been applied successfully to global optimization problems. However, genetic operators in their standard forms are blind to the presence of constraints. Thus, the extension of GAs to constrained optimization problems by incorporating suitable handing techniques is an active direction within GAs research. Recently, we have proposed a Binary Real coded Genetic Algorithm (BRGA). BRGA is a new hybrid approach that combines cooperative Binary coded GA (BGA) with Real coded GA (RGA). It employs an adaptive parameter-based hybrid scheme that distributes the computational power and regulates the interactions between the cooperative versions, which operate in a sequential time-interleaving manner. In this study, we aim to extend BRGA to constrained problems by introducing a modified dynamic penalty function into the architecture of BRGA. We use the CEC'2010 benchmark suite of 18 functions to analyze the quality, time and scalability performance of BRGA. To investigate the effectiveness of the proposed modification, we compare the performance of BRGA under both the original and the modified penalty functions. Moreover, to demonstrate the performance of BRGA, we compare it with the performance of some other EAs from the literature. We also implement a robust parameter tuning procedure that relies on techniques from statistical testing, experimental design and Response Surface Methodology (RSM) to estimate the optimal values for the control parameters to secure a good performance by BRGA against specific problems at hand.