An adaptive genetic algorithm of service selection in pervasive computing is presented in this paper. By means of matrix encoding, this algorithm carries out selection, crossover and mutation operations of genetic algorithm, with matrix as individual chromosome and matrix array as gene. Based on the elitism selection strategy and adaptive strategy, this algorithm replicates the optimal individu...

Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions and adequate fitness function it is possible to change the music composing into an optimization problem. This paper proposes a new genetic algorithm for composing music. Considering...

Clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. In recent years, several algorithms are provided based on evolutionary algorithms for cluster...

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based...

We define the linkage model evolvability and the evolvabilitybased fitness distance correlation. These measures give an insight in the search characteristics of linkage model building genetic algorithms. We apply them on the linkage tree genetic algorithm for deceptive trap functions and the nearest-neighbor NK-landscape problem. Comparisons are made between linkage trees, based on mutual infor...

This paper introduces the Pareto front as a useful analysis tool to explore the design space of MOS Current Mode Logic (MCML) circuits. A genetic algorithm (GA) is employed to automatically detect this front in a process that efficiently finds optimal parameterizations and their corresponding values in an aggregate fitness space. As an example of the flexibility of this design automation approa...

In addition to GA-deception, the lack of fitness differences among low-order schemata can also degrade GA’s search. Therefore, a coding should present adequate superior low-order building blocks at the early stage of search. This paper aims to reveal the inherent periodicity in the search process of a genetic algorithm, and to show how to make use of this periodicity in the design of representa...

This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their in...

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a fun...