Particle filtering (PF) is a sequential Monte Carlo method that draws sample (particle) values
of state variables of interest to approximate the posterior probability distribution function …

Buildings are an essential part of our social life. People spend a substantial fraction of their
time and spend a high amount of energy in them. There is a grand variety of systems and …

Over the last few decades, many improved Evolutionary Algorithms (EAs) have been
proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among …

In several combinatorial optimization problems arising in cryptography and design theory,
the admissible solutions must often satisfy a balancedness constraint, such as being …

The performance of most evolutionary metaheuristic algorithms relies on various operators.
The crossover operator is a standard based on population-based algorithms, which is …

Differential Evolution (DE) is currently one of the most competitive Evolutionary Algorithms
(EAs) for optimization problems involving continuous parameters. This article presents three …

Modern enterprise systems can be composed of many web services (eg, SOAP and
RESTful). Users of such systems might not have direct access to those services, and rather …

The aim of Neuroevolution is to find neural networks and convolutional neural network
(CNN) architectures automatically through evolutionary algorithms. A crucial problem in …

Considering the millions of end-users in a medium-scale city, even achieving a smart price
improvement or imposing incentives for flattening the demands makes millions of saving …

Crossover is an important operator in genetic algorithms. Although hundreds of application
dependent and independent crossover operators exist in the literature, this chapter provides …