Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Recent developments and trends in global optimization
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Infodynamics: Analogical analysis of states of matter and information
Information Sciences: an International Journal
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
Population size reduction for the differential evolution algorithm
Applied Intelligence
Balancing Population- and Individual-Level Adaptation in Changing Environments
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Preserving and exploiting genetic diversity in evolutionary programming algorithms
IEEE Transactions on Evolutionary Computation
Lipschitz and Hölder global optimization using space-filling curves
Applied Numerical Mathematics
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
Particle swarm algorithm with hybrid mutation strategy
Applied Soft Computing
An Intelligent Tuned Harmony Search algorithm for optimisation
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Diversity Management in Evolutionary Many-Objective Optimization
IEEE Transactions on Evolutionary Computation
Diversity Through Multiculturality: Assessing Migrant Choice Policies in an Island Model
IEEE Transactions on Evolutionary Computation
Tabu search with multi-level neighborhood structures for high dimensional problems
Applied Intelligence
LADPSO: using fuzzy logic to conduct PSO algorithm
Applied Intelligence
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
A modified gravitational search algorithm for slope stability analysis
Engineering Applications of Artificial Intelligence
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Multi-circle detection on images inspired by collective animal behavior
Applied Intelligence
Adaptive cooperative particle swarm optimizer
Applied Intelligence
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The ability of an Evolutionary Algorithm (EA) to find a global optimal solution depends on its capacity to find a good rate between exploitation of found-so-far elements and exploration of the search space. Inspired by natural phenomena, researchers have developed many successful evolutionary algorithms which, at original versions, define operators that mimic the way nature solves complex problems, with no actual consideration of the exploration-exploitation balance. In this paper, a novel nature-inspired algorithm called the States of Matter Search (SMS) is introduced. The SMS algorithm is based on the simulation of the states of matter phenomenon. In SMS, individuals emulate molecules which interact to each other by using evolutionary operations which are based on the physical principles of the thermal-energy motion mechanism. The algorithm is devised by considering each state of matter at one different exploration---exploitation ratio. The evolutionary process is divided into three phases which emulate the three states of matter: gas, liquid and solid. In each state, molecules (individuals) exhibit different movement capacities. Beginning from the gas state (pure exploration), the algorithm modifies the intensities of exploration and exploitation until the solid state (pure exploitation) is reached. As a result, the approach can substantially improve the balance between exploration---exploitation, yet preserving the good search capabilities of an evolutionary approach. To illustrate the proficiency and robustness of the proposed algorithm, it is compared to other well-known evolutionary methods including novel variants that incorporate diversity preservation schemes. The comparison examines several standard benchmark functions which are commonly considered within the EA field. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration---exploitation balance.