The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new or apply existing ones problems. To simplify the development and application algorithms, there has been an increasing interest in their automatic design from components. These automatically designed metaheuristics can outperform human-developed counterparts. However, is still ...

Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair-wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA-ES, which has found many applications in solving continuous optimizatio...

the purpose of this study is to demonstrate the potential of imperialist competitivealgorithm (ica) for solving blasius dierential equation. this algorithm is inspiredby competition mechanism among imperialists and colonies and has demonstrated excellentcapabilities such as simplicity, accuracy, faster convergence and better global optimumachievement in contrast to other evolutionary algorithms...

A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...

This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes within evolutionary systems. Following a summary of the significant features of embryogenies, the three main types of embryogenies in Evolutionary Computation are then identified and explained: external, explicit and implicit. An experimental comparison between these three different embryogenies and...

In this paper we describe how we have developed an application that is capable of evolving melodies with some human help to guide it. We have developed this system using evolutionary algorithms and interactive evolution. The user can use the application to, first, evolve short blocks of notes, and second, to combine these blocks into longer melodies using more evolutionary techniques. The resul...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

This thesis proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the popu...

During the past three decades, global optimization problems (including single-objective optimization problems (SOP) and multi-objective optimization problems (MOP)) have been intensively studied not only in Computer Science, but also in Engineering. There are many solutions in literature, such as gradient projection method [1-3], Lagrangian and augmented Lagrangian penalty methods [4-6], and ag...

this research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. this objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. moreover, this type of problem is np-hard and solving this probl...