Hasançebi, O., Erbatur, F.: Evaluation of crossover techniques in genetic algorithm based optimum structural design. Computers & Structures 78 (1–3), 435–448 (2000)Hasancebi O, Erbatur F. Evaluation of cros
The CIGA is tested on 14 benchmark functions conjointly with the other existing optimization techniques to establish its superiority. Finally, the CIGA is applied to the practical optimization problem of synthesizing non-uniform linear antenna arrays with low side lobe levels (SLL) and low beam ...
Genetic Algorithms Crossover - Explore the various crossover techniques in genetic algorithms, including one-point, two-point, and uniform crossover methods, to enhance your algorithm's performance.
Cognitive Techniques: Physical and Link Layers Step 2b. Crossover Crossover is performed on two parents to form two new offspring. The GA has a crossover probability that determines if crossover will happen. A randomly generated floating-point value is compared to the crossover probability, and...
Notable crossover techniques include the single-point, the two-point, and the uniform types [23]. Mutation involves the modification of the value of each ‘gene’ of a solution with some probability m p , (the mutation probability). The role of mutation in GAs has been that of ...
In this paper, we propose a hybrid genetic algorithm, HGA, for the RCPSP. HGA introduces several changes in the GA paradigm. For this reason, we say that the resulting algorithm is Hybrid Genetic Algorithm (HGA). These changes are inspired by the techniques successfully used by ...
Genetic algorithm (GA) is used to solve a variety of optimization problems. Mutation operator also is responsible in GA for maintaining a desired level of
Network intrusion detection system plays a vital role in today`s network. In this system using Genetic Algorithm (GA), crossover, mutation and other related techniques to used to detect network intrusion system. As the transmission of data over the Internet increases, the need to protect ...
Genetic algorithms are flexible for working with arbitrary restrictions and optimizing multiple functions with conflicting objectives. They are also easily hybridized with other techniques and heuristics (Michalewicz and Fogel, 1999; Mitchell, 1996). View article Chapter Hybrid computational intelligence for ...
Further, it is seen that the genetic algorithm (GA) is used to develop GA-Fisher algorithm for FR systems [15]. The GA employs crossover and mutation techniques for the optimal search process. The disadvantages with the GA are that the offspring never ends at the same location as their pa...