Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Discrete structural optimization
Discrete structural optimization
Improved genetic operators for structural engineering optimization
Advances in Engineering Software
Parallel processing neural networks and genetic algorithms
Advances in Engineering Software
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A new adaptive penalty scheme for genetic algorithms
Information Sciences: an International Journal - Special issue: Evolutionary computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
Modelling and Simulation in Engineering
Weight minimization of trusses with genetic algorithm
Applied Soft Computing
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
Singular optimum topology of skeletal structures with frequency constraints by AGGA
Structural and Multidisciplinary Optimization
Genetic algorithm-based charging task scheduler for electric vehicles in smart transportation
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis
International Journal of Computational Science and Engineering
Fully Stressed Design Evolution Strategy for Shape and Size Optimization of Truss Structures
Computers and Structures
Krill herd algorithm for optimum design of truss structures
International Journal of Bio-Inspired Computation
Genetic algorithm-based demand response scheme for electric vehicle charging
International Journal of Intelligent Information and Database Systems
Chaotic swarming of particles: A new method for size optimization of truss structures
Advances in Engineering Software
Impact of static and adaptive mutation techniques on the performance of Genetic Algorithm
International Journal of Hybrid Intelligent Systems
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The performance of genetic algorithms (GA) is affected by various factors such as coefficients and constants, genetic operators, parameters and some strategies. Member grouping and initial population strategies are also examples of factors. While the member grouping strategy is adopted to reduce the size of the problem, the initial population strategy is applied to reduce the number of search to reach the optimum design in the solution space. In this study, two new self-adaptive member grouping strategies, and a new strategy to set the initial population are discussed. Previously proposed self-adaptive approaches for both the penalty function and the mutation and crossover operators are also adopted in the design. The effect of the proposed strategies on the performance of the GA for capturing the global optimum is tested on the optimization of 2d and 3d truss structures. It is worthy to say that the proposed strategies reduce the number of searches within the solution space and enhance the convergence capability and the performance of the GA.