This article studies the performance of two metaheuristics, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biologi...

In response to the anthropogenic assault of toxic baits, populations of the German cockroach have rapidly evolved an adaptive behavioral aversion to glucose (a phagostimulant component of baits). We hypothesized that changes in the peripheral gustatory system are responsible for glucose aversion. In both wild-type and glucose-averse (GA) cockroaches, D-fructose and D-glucose stimulated sugar-gu...

In this chapter, the essentials of genetic algorithm (GA) following the footsteps of Turing are introduced. We introduce the connection between Turing’s early ideas of organized machines and modern evolutionary computation. We mainly discuss the GA applications to adaptive complex system modeling. We study the agent-based market where collective behaviors are regarded as aggregations of individ...

It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle (PSO), genetic algorithm (GA) ant colony (ACO) are selected be applied STE. After calculation, all can obtain le...

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one “optimal choice” rather the situation changes per problem instance and during evolution. This paper examines whether this choice can be left...

This paper proposes a genetic algorithm (GA) for path planning of an autonomous mobile robot in the 3-D space which is modeled with a grid structure. We present a GA for 2-D path planning and then generalize it to 3-D path planning. Some simulation results are also presented. The GA has two unique features. First, the dimension has only small e ect on time and space requirements. Second, it is ...

The performance of GA is measured and analyzed in terms of its performance parameters against variations in its genetic operators and associated parameters. Since last four decades huge numbers of researchers have been working on the performance of GA and its enhancement. This earlier research work on analyzing the performance of GA enforces the need to further investigate the exploration and e...

A self-aware signal processing architecture is proposed based on adaptive resource escalation which is guided by a multi-objective Genetic Algorithm (GA). TheGAprioritizes taskswithin a reconfigurable hardware fabric to maintain the quality-of-service and power consumption objectives. Attainment of these objectives is subject to the intrinsic reliability and performance of the computational ele...