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What is Simulated Annealing

Handbook of Research on Artificial Intelligence Techniques and Algorithms
A probabilistic method to find global minimum of a cost function that may possess several local minima.
Published in Chapter:
Cancer Biomarker Assessment Using Evolutionary Rough Multi-Objective Optimization Algorithm
Anasua Sarkar (Government College of Engineering and Leather Technology, India) and Ujjwal Maulik (Jadavpur University, India)
DOI: 10.4018/978-1-4666-7258-1.ch016
Abstract
A hybrid unsupervised learning algorithm, which is termed as Evolutionary Rough Multi-Objective Optimization (ERMOO) algorithm, is proposed in this chapter. It comprises a judicious integration of the principles of the rough sets theory with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competence measurement, it enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classification method in this algorithm. A measure of the amount of domination between two solutions is incorporated in this chapter to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. The performance is demonstrated on real-life breast cancer dataset for identification of Cancer Associated Fibroblasts (CAFs) within the tumor stroma, and the identified biomarkers are reported. Moreover, biological significance tests are carried out for the obtained markers.
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More Results
Stochastic Approximation Monte Carlo for MLP Learning
A generic probabilistic meta-algorithm used to find true or approximate solutions to global optimization problems.
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A Comparison of Cooling Schedules for Simulated Annealing
Simulated Annealing (SA) is a variant of the metaheuristic of local search that incorporates a stochastic criterion of acceptance of worse quality solutions, in order to prevent the algorithm from being prematurely trapped in local optima
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Decision-Making for Biomass Harvesting Routing by using the Simulated Annealing
A probabilistic approximation technique that allows finding the global optimum value of a given function solution.
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Probability and Strategies
A probabilistic technique for approximating the global optimum.
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Supply Chain Design Including Quality Considerations: Modeling and Solution Approaches based on Metaheuristics
A metaheuristic based on a local search that escapes local optima to search new regions by being able to accept inferior solutions by means of a probabilistic process.
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Realizing the Need for Intelligent Optimization Tool
It is an optimization algorithm developed by artificially modeling the cooling process of molten metal.
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Melanoma Identification Using MLP With Parameter Selected by Metaheuristic Algorithms
A popular single solution metaheuristic algorithm which has a background of statistical mechanics.
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Hybrid Optimization Techniques for Industrial Production Planning: A Review
SA is a black box stochastic algorithm that generates a sequence of random solutions converging to a global optimum.
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