Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and lo...

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strate...

Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-diierentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy...

Mixed strategy evolutionary algorithms (EAs) aim at integrating several mutation operators into a single algorithm. However no analysis has been made to answer the theoretical question: whether and when is the performance of mixed strategy EAs better than that of pure strategy EAs? In this paper, asymptotic convergence rate and asymptotic hitting time are proposed to measure the performance of ...

A mutation, GD-1, in the leucine operon imposed unusual growth characteristics upon a leucine auxotrophic strain bearing the leucine operator mutation, leu-500. The strain with the GD-1 mutation was able to grow on a minimal salts medium when citrate was the sole carbon source, but required leucine when glucose was present. Tests with a large number of carbohydrates suggest that in the strain b...

Particle swarm optimization (PSO) method is relatively a new population-based intelligence algorithm and exhibits good performance in optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged possibly around the local solution. PSO te...

Mutation analysis is a powerful but computationally expensive method to measure the effectiveness of a testing or debugging technique. The high cost is due, in part, to redundant mutants generated by commonly used mutation operators. A mutant is said to be redundant if its outcome can be predicted based on the outcome of other mutants. The execution of those redundant mutants is unnecessary and...

In this paper, we propose a chaos-based multiobjective immune algorithm (CMIA) with a fine-grained selection mechanism based on the clonal selection principle. Taking advantage of the ergodic and stochastic properties of chaotic sequence, a novel mutation operator, named as chaos-based mutation (CM) operator, is proposed. Moreover, the information of diversity estimation is also adopted in the ...