Evolutionary Algorithms in Intelligent Systems

How can a search algorithm help tune a neural network, navigate a difficult optimization problem, or allocate tasks across a wireless sensor network? Evolutionary Algorithms in Intelligent Systems brings together research exploring these questions through evolutionary computation and related metaheuristics. This is a specialist research collection, with examples grounded in the design and optimization of intelligent systems.

Optimization methods in practical research

The volume covers approaches including differential evolution and particle swarm optimization, along with work on multi-objective optimization and meta-optimization. Across its papers, the emphasis is on how algorithmic strategies can be applied to problems where finding an effective approximate solution is important.

From neural networks to network tasks

Topics include neural-network optimization, machine-learning feature selection, association-rule mining, and task allocation in wireless sensor networks. The range makes the collection useful for seeing how evolutionary methods move between core optimization questions and applications in computing and intelligent systems.

A research collection, not a step-by-step course

As a reprint of a special issue of Mathematics, this edited volume presents separate scholarly contributions rather than a single continuous textbook. Readers interested in computational intelligence, evolutionary computation, optimization, or machine learning can consult it for research examples and perspectives on the methods discussed.