Algorithms for solving hard optimization problems typically have several parameters that
need to be set appropriately such that some aspect of performance is optimized. In this …

Parameters are an integral part of every metaheuristic algorithm as these control different
aspects of algorithms. Thus, parameter tuning (PT) approaches are one of the prime sub …

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics,
Biologically Inspired Computation and Computational Intelligence that have been described …

Metaheuristics are a relatively new but already established approach to combinatorial
optimization. A metaheuristic is a generic algorithmic template that can be used for finding …

Several techniques have been proposed to tackle the Adaptive Operator Selection (AOS)
issue in Evolutionary Algorithms. Some recent proposals are based on the Multi-armed …

Optimization and its related solving methods are becoming increasingly important in most
academic and industrial fields. The goal of the optimization process is to make a system or a …

Uncertainty arises in reinforcement learning from various sources, and therefore it is
necessary to consider statistics based on several roll-outs for evaluating behavioral policies …

Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets
that are non-redundant and are representative of typical optimization scenarios. In this …

Meta-heuristic algorithms should be compared using the best parameter values for all the
involved algorithms. However, this is often unrealised despite the existence of several …

Autonomous search is a particular case of adaptive systems that improve their solving
performance by modifying and adjusting themselves to the problem at hand, either by self …