Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The nature of statistical learning theory
The nature of statistical learning theory
Tabu Search
Journal of Global Optimization
Hints for Adaptive Problem Solving Gleaned from Immune Networks
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A modified particle swarm optimization predicted by velocity
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Bat algorithm for multi-objective optimisation
International Journal of Bio-Inspired Computation
Optimal design of constraint engineering systems: application of mutable smart bee algorithm
International Journal of Bio-Inspired Computation
International Journal of Computational Science and Engineering
Social emotional optimisation algorithm with emotional model
International Journal of Computational Science and Engineering
A comparison of nature inspired algorithms for multi-threshold image segmentation
Expert Systems with Applications: An International Journal
Light responsive curve selection for photosynthesis operator of APOA
International Journal of Bio-Inspired Computation
APOA with parabola model for directing orbits of chaotic systems
International Journal of Bio-Inspired Computation
A new hybrid algorithm for unconstrained optimisation problems
International Journal of Computer Applications in Technology
Bat algorithm: literature review and applications
International Journal of Bio-Inspired Computation
International Journal of Metaheuristics
Using Watts-Strogatz particle swarm optimisation to solve direct orbits of chaotic systems
International Journal of Computing Science and Mathematics
International Journal of Bio-Inspired Computation
Bio-inspired computation: success and challenges of IJBIC
International Journal of Bio-Inspired Computation
Ant colony optimisation for vehicle traffic systems: applications and challenges
International Journal of Bio-Inspired Computation
An empirical study of test effort estimation based on bat algorithm
International Journal of Bio-Inspired Computation
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Meta-heuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimisation problems. More than a dozen major meta-heuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrids of meta-heuristics. This paper intends to provide an overview of nature-inspired meta-heuristic algorithms, from a brief history to their applications. We try to analyse the main components of these algorithms and how and why they work. Then, we intend to provide a unified view of meta-heuristics by proposing a generalised evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.