This paper presents a hybrid of genetic algorithm (GA) and local search operators for solving the three-matching problem (3MP). Several general / heuristic crossover and local hill-climbing operators are introduced. The adaptive process based on the observation of on-line performance of GA is used to select the feasible operators. In particular, the probability of applying an operator is increa...

Objective: The purpose of this study was to investigate the relationship between adaptive and maladaptive cognitive emotion regulation strategies, self-compassion, and self-esteem; and to determine whether self-compassion compared to self-esteem, was a better predictor of the scores on the adaptive and maladaptive cognitive emotion regulation strategies.  Methods: This was a cross-section...

In this paper novel technique for CPU scheduling in real time operating systems by using genetic algorithm (GA) is proposed. Proposed adaptive algorithm is a combination of existing dynamic priority driven algorithm i.e. Earliest Deadline First (EDF) and new genetic algorithm (GA) based scheduling algorithm. First we have developed GA based scheduling algorithm and tested it during both under l...

In this paper, an enhanced version of the salp swarm algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) Diversification SSA population referred as $$_{std}$$ , (ii) parameters are tuned using a self-adaptive technique-based genetic (GA) $$_{GA-tuner}$$ . The novelty developing is to enhance its performance through balancing search exploratio...

Constrained optimization via the Genetic Algorithm (GA) is often a challenging endeavor, as the GA is most directly suited to unconstrained optimization. Traditionally, external penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. This approach requires the somewhat arbitrary selection of penalty draw-down coeffi...

In this paper the well-known 0-1 Multiple Knapsack Problem (MKP) is approached by an adaptive Binary Differential Evolution (aBDE) algorithm. The MKP is a NP-hard optimization problem and the aim is to maximize the total profit subjected to the total weight in each knapsack that must be less than or equal to a given limit. The aBDE self adjusts two parameters, perturbation and mutation rates, u...

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the t...