The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Le´vy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95 confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95 confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90 confidence level.
@cite_33 adopted a genetic algorithm (GA) based on natural selection. Initially, the GA begins with randomly created test cases called chromosomes. These chromosomes undergo crossover and mutation until the termination criterion is met. In each cycle, the best chromosomes are probabilistically selected and added to the final test suite.
@cite_33
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We propose a new approach for designing classifiers for a c-class (c spl ges 2) problem using genetic programming (GP). The proposed approach takes an integrated view of all classes when the GP evolves. A multitree representation of chromosomes is used. In this context, we propose a modified crossover operation and a new mutation operation that reduces the destructive nature of conventional genetic operations. We use a new concept of unfitness of a tree to select trees for genetic operations. This gives more opportunity to unfit trees to become fit. A new concept of OR-ing chromosomes in the terminal population is introduced, which enables us to get a classifier with better performance. Finally, a weight-based scheme and some heuristic rules characterizing typical ambiguous situations are used for conflict resolution. The classifier is capable of saying "don't know" when faced with unfamiliar examples. The effectiveness of our scheme is demonstrated on several real data sets. Form generation or morphogenesis is one of the main stages of both artificial and natural development. This paper provides results from experiments in which a genetic algorithm (GA) was used to evolve cellular automata (CA) to produce predefined 2D and 3D shapes. The GA worked by evolving the CA rule table and the number of iterations that the model was to run. After the final chromosomes were obtained for all shapes, the CA model was allowed to run starting with a single cell in the middle of the lattice until the allowed number of iterations was reached and a shape was formed. In all cases, mean fitness values of evolved chromosomes were above 80. As an important coordination and cooperation mechanism in multi-agent systems, coalition of agents exhibits some excellent characteristics and draws researchers' attention increasingly. Cooperation formation has been a very active area of research in multi-agent systems. An efficient algorithm is needed for this topic since the numbers of the possible coalitions are exponential in the number of agents. Genetic algorithm (GA) has been widely reckoned as a useful tool for obtaining high quality and optimal solutions for a broad range of combinatorial optimization problems due to its intelligent advantages of self-organization, self-adaptation and inherent parallelism. This paper proposes a GA-based algorithm for coalition structure formation which aims at achieving goals of high performance, scalability, and fast convergence rate simultaneously. A novel 2D binary chromosome encoding approach and corresponding crossover and mutation operators are presented in this paper. Two valid parental chromosomes are certain to produce a valid offspring under the operation of the crossover operator. This improves the efficiency and shortens the running time greatly. The proposed algorithm is evaluated through a robust comparison with heuristic search algorithms. We have confirmed that our new algorithm is robust, self-adaptive and very efficient by experiments. The results of the proposed algorithm are found to be satisfactory. Beginning with the early days of the genetic algorithm and the schema theorem it has often been argued that the crossover operator is the more important genetic operator. The early Royal Road functions were put forth as an example where crossover would excel, yet mutation based EAs were subsequently shown to experimentally outperform GAs with crossover on these functions. Recently several new Royal Roads have been introduced and proved to require expected polynomial time for GAs with crossover, while needing exponential time to optimize for mutation-only EAs. This paper does the converse, showing proofs that GAs with crossover require exponential optimization time on new Ignoble Trail functions while mutation based EAs optimize them efficiently.