2 Representations for Genetic and Evolutionary Algorithms Page 1 2 Representations for Genetic
and Evolutionary Algorithms In this second chapter, we present an introduction into the field of …

The fundamental design choices in an evolutionary algorithm (EA) are its representation of
candidate solutions and the operators that will act on that representation. We propose …

In recent years, a gradual increase in the sophistication of multiobjective evolutionary
algorithms (MOEAs) for Pareto optimization has been seen, accompanied by an ever …

Recently, researchers introduced energy-efficient dynamic routing protocols for wireless
sensor networks to avoid the premature end of network lifetime. This paper addresses the …

In this paper, we propose a micro genetic algorithm with three forms of elitism for
multiobjective optimization. We show how this relatively simple algorithm coupled with an …

Distribution system problems, such as planning, loss minimization, and energy restoration,
usually involve network reconfiguration procedures. The determination of an optimal …

[Selection] Select two parent chromosomes from a population according to their fitness (the
better fitness, the bigger chance to be selected)[Crossover] With a crossover probability …

The most important element in the design of a decoder-based evolutionary algorithm is its
genotypic representation. The genotypedecoder pair must exhibit efficiency, locality, and …

Abstract A Wireless Sensor Network (WSN) is a group of tiny power-constrained nodes that
cover a vast region of interest (ROI), sense and communicate it to the Base Station (BS). The …

Network design involves several areas of engineering and science. Computer networks,
electrical circuits, transportation problems, and phylogenetic trees are some examples. In …