Inspired by the evolutionary strategy and the biological DNA mechanism, a hybrid DNA based genetic algorithm (HDNA-GA) with the population update operation and the adaptive parameter scope operation is proposed for solving parameter estimation problems of dynamic systems. The HDNA-GA adopts the nucleotides based coding and some molecular operations. In HDNA-GA, three new crossover operators, re...

This paper proposes a modified sine cosine algorithm (MSCA) for discrete sizing optimization of truss structures. The original sine cosine algorithm (SCA) is a population-based metaheuristic that fluctuates the search agents about the best solution based on sine and cosine functions. The efficiency of the original SCA in solving standard optimization problems of well-known mathematical function...

in this paper, the optimal location and characteristics of tadas dampers in moment resisting steel structures, considering the application of minimum number of tadas dampers in a building as an objective function and the restriction for destruction of main members is studied. genetic algorithm in first generation randomly produces different chromosomes representing unique tadas dampers distribu...

In this paper we address the problem of program discovery as deened by Genetic Programming 10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer tness evaluations and a greater probability of a success than Geneti...

The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” function. In that process, crossover operator plays an important role. To comprehend the GAs as a whole, it is necessary to understand ...

Multi-objective problems with parameter interactions can present difficulties to many optimization algorithms. We have investigated the behaviour of Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX) operator within the NSGA-II (Non-dominated So...

In this paper, chaos based a new arithmetic crossover operator on the genetic algorithm has been proposed. The most frequent issue for the optimization algorithms is stuck on problem's defined local minimum points and it needs excessive amount of time to escape from them; therefore, these algorithms may never find global minimum points. To avoid and escape from local minimums, a chaotic arithme...

In this paper we propose two new methods for implementing the mutation operator in Genetic Programming called Semantic Aware Mutation (SAM) and Semantic Similarity based Mutation (SSM). SAM is inspired by our previous work on a semantics based crossover called Semantic Aware Crossover (SAC) [19] and SSM is an extension of SAM by adding more control on the change of semantics of the subtrees inv...

Because of the use of growing information, web mining has become a primary necessity of world. Due to this, research on web mining has received a lot of interest from both industry and academia. Mining and prediction of user’s web browsing behaviors and deducing the actual content in a web document is one of the active subjects. The information on web is dirty. Apart from useful information, it...

Nowadays, it is very popular to employ genetic algorithm (GA) and its improved strategies optimize neural networks (i.e., WNN) solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, traditional GA only focuses on restraining infinite growth optimal species without reducing similarity among remaining excellent individuals when using exclusion operator. Additio...