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Local optimum

Known as: Local optima, Local optimality, Locally optimal 
In applied mathematics and computer science, a local optimum of an optimization problem is a solution that is optimal (either maximal or minimal… Expand
Wikipedia

Related topics

Related topics

26 relations
AlgorithmAutomatic label placementBroyden–Fletcher–Goldfarb–Shanno algorithmCluster analysis
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Broader (1)

Mathematical optimization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Tabu Search
  • M. Laguna
  • Handbook of Heuristics
  • 2018
    • Corpus ID: 2867322
From the Publisher: This book explores the meta-heuristics approach called tabu search, which is dramatically changing our… Expand
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Highly Cited
2014
Highly Cited
2014
Grey Wolf Optimizer
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm… Expand
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Highly Cited
2008
Highly Cited
2008
Biogeography-Based Optimization
  • D. Simon
  • IEEE Transactions on Evolutionary Computation
  • 2008
    • Corpus ID: 8319014
Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the… Expand
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Highly Cited
2007
Highly Cited
2007
Adaptive Service Composition in Flexible Processes
In advanced service oriented systems, complex applications, described as abstract business processes, can be executed by invoking… Expand
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Highly Cited
2004
Highly Cited
2004
Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study
In this article, we study Pareto local optimum sets for the biobjective Traveling Salesman Problem applying straightforward… Expand
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Highly Cited
2000
Highly Cited
2000
The Clonal Selection Algorithm with Engineering Applications 1
The clonal selection algorithm is used by the natural immune system to define the basic features of an immune response to an… Expand
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Highly Cited
1999
Highly Cited
1999
Policy Gradient Methods for Reinforcement Learning with Function Approximation
Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and… Expand
Highly Cited
1998
Highly Cited
1998
Deterministic annealing for clustering, compression, classification, regression, and related optimization problems
The deterministic annealing approach to clustering and its extensions has demonstrated substantial performance improvement over… Expand
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Highly Cited
1992
Highly Cited
1992
SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing
This module accompanies the Computer Science in Economics & Management article "Simulated Annealing: An Initial Application in… Expand
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Highly Cited
1985
Highly Cited
1985
How easy is local search?
We investigate the complexity of finding locally optimal solutions to NP-hard combinatorial optimization problems. Local… Expand
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