Search-Space Partitioning for Parallelizing SMT Solvers

2015 ◽  
pp. 369-386 ◽  
Author(s):  
Antti E. J. Hyvärinen ◽  
Matteo Marescotti ◽  
Natasha Sharygina
Keyword(s):  
Search Space ◽  
Space Partitioning ◽  
Smt Solvers

Search Space Partitioning to Enhance Outlier Rule Discovery

2002 ◽  
pp. 259-263
Author(s):  
Michał Okoniewski ◽  
Piotr Gawrysiak ◽  
Łukasz Gancarz
Keyword(s):  
Search Space ◽  
Rule Discovery ◽  
Space Partitioning

scholarly journals Parallelization algorithms for modeling ARM processes

2000 ◽  
Vol 13 (4) ◽  
pp. 393-410
Author(s):  
Benjamin Melamed ◽  
Santokh Singh
Keyword(s):  
Stochastic Processes ◽  
Objective Function ◽  
Empirical Distribution ◽  
Sample Path ◽  
Real Life ◽  
Search Space ◽  
Global Search ◽  
Communication Overhead ◽  
Space Partitioning ◽  
Nonlinear Objective Function

AutoRegressive Modular (ARM) processes are a new class of nonlinear stochastic processes, which can accurately model a large class of stochastic processes, by capturing the empirical distribution and autocorrelation function simultaneously. Given an empirical sample path, the ARM modeling procedure consists of two steps: a global search for locating the minima of a nonlinear objective function over a large parametric space, and a local optimization of optimal or near optimal models found in the first step. In particular, since the first task calls for the evaluation of the objective function at each vector of the search space, the global search is a time consuming procedure. To speed up the computations, parallelization of the global search can be effectively used by partitioning the search space among multiple processors, since the requisite communication overhead is negligible.This paper describes two space-partitioning methods, called Interleaving and Segmentation, respectively. The speedups resulting from these methods are compared for their performance in modeling real-life data.

scholarly journals Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning

2020 ◽  
Vol 17 (3) ◽  
pp. 175-188
Author(s):  
H.A. Bashir
Keyword(s):  
Global Optimization ◽  
Evolutionary Computation ◽  
Search Space ◽  
Spatial Diversity ◽  
Optimal Solutions ◽  
Space Partitioning ◽  
Control Policies ◽  
The Poor ◽  
Diversity Control ◽  
Exploration Exploitation

Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes. Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy.

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