Fujun LAN, Liang GAO Department of Industrial & Systems Engineering Huazhong University .of Science &Technology 430074, Wuhan, P. R. China Abstract: Layout optimization is a NP-hard and complex non-linear constrained optimization problem. To solve the problem, this paper proposes a Modified Electromagnetism-like Mechanism (MEM) algorithm. Firstly, the established local search method is modified...

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 ...

Since knowledge graphs (KGs) describe and model the relationships between entities concepts in real world, reasoning on KGs often correspond to reachability queries with label substructure constraints (LSCR queries). Specifically, for a search path p, LSCR not only require that labels of edges passed by p are certain set, but also claim vertex could satisfy constraint. They much more complex th...

This paper presents a hybrid evolutionary algorithm (EA) for developing locomotion gait of Sony legged robots. The selection of EA parameters such as the population size and recombination methods is made to be flexible and strive towards optimal performance autonomously. An interactive software environment with an overhead CCD camera is used to evaluate the performance of the generated gaits. T...

One hope when using non-elitism in evolutionary computation is that the ability to abandon current-best solution aids leaving local optima. To improve our understanding of this mechanism, we perform a rigorous runtime analysis basic non-elitist algorithm (EA), $$(\mu ,\lambda )$$ EA, on most benchmark function with optimum, jump function. We prove for all reasonable values parameters and proble...

This paper describes the operation of an Evolutionary Algorithm (EA) for the creation of linear digital VLSI circuit designs. The EA can produce hardware designs from a behavioural description of a problem. The designs are based upon a library of high-level components. The EA performs a multi-objective search, using models of the longest-path delay and the silicon area of a design. These models...

In this paper, we study the conditions in which the (1+1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of tness distribution function at given iteration and the average optimization time. Our approach is applicable when the reproduction operator of an evolutionary algorithm is dominated by the mutation operator of the (1+1)-EA. In this case one can extend the lower boun...

In this paper, we study the conditions in which the (1+1)-EA compares favorably to other evolutionary algorithms (EAs) in terms of fitness function distribution at given iteration and with respect to the average optimization time. Our approach is applicable when the reproduction operator of an evolutionary algorithm is dominated by the mutation operator of the (1+1)-EA. In this case one can ext...

Some experimental investigations have shown that evolutionary algorithms (EAs) are efficient for the minimum label spanning tree (MLST) problem. However, we know little about that in theory. As one step towards this issue, we theoretically analyze the performances of the (1+1) EA, a simple version of EAs, and a multi-objective evolutionary algorithm called GSEMO on the MLST problem. We reveal t...

Different fitness functions describe different problems. Hence, certain fitness transformations can lead to easier problems although they are still a model of the considered problem. In this paper, the class of neutral transformations for a simple rank-based evolutionary algorithm (EA) is described completely, i.e., the class of functions that transfers easy problems for this EA in easy ones an...