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A comparison of semi-deterministic and stochastic search techniques

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Andy M. Connor, Kristina Shea

Neural and Evolutionary Computing · Submitted 18 May 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim These performances are then compared to each other as and to a steepest descent (SD) method.

how it is stated · asserted 0.95 read the record →

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arXiv ID1605.05782 (version 1)
Pith IDJXBNRB7Z, Pith's permanent record of this paper and its review
CitationAndy M. Connor, Kristina Shea. "A comparison of semi-deterministic and stochastic search techniques." arXiv:1605.05782 (2016). https://pith.science/paper/JXBNRB7Z
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,842 input tokens.

What the abstract itself states, as probabilities: claims first 0.02 · states limitation 0.11 · human participants 0.02 · controlled comparison 0.84 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.95 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper presents an investigation of two search techniques, tabu search (TS) and simulated annealing (SA), to assess their relative merits when applied to engineering design optimisation. Design optimisation problems are generally characterised as having multi-modal search spaces and discontinuities making global optimisation techniques beneficial. Both techniques claim to be capable of locating globally optimum solutions on a range of problems but this capability is derived from different underlying philosophies. While tabu search uses a semi-deterministic approach to escape local optima, simulated annealing uses a complete stochastic approach. The performance of each technique is investigated using a structural optimisation problem. These performances are then compared to each other as and to a steepest descent (SD) method.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper presents an investigation of two search techniques, tabu search (TS) and simulated annealing (SA), to assess their relative merits when applied to engineering design optimisation. contribution 1.00 0.85 0.04 asserted 0.92
s1 Design optimisation problems are generally characterised as having multi-modal search spaces and discontinuities making global optimisation techniques beneficial. background 1.00 0.06 0.02 asserted 0.95
s2 Both techniques claim to be capable of locating globally optimum solutions on a range of problems but this capability is derived from different underlying philosophies. background 0.92 0.10 0.04 asserted 0.88
s3 While tabu search uses a semi-deterministic approach to escape local optima, simulated annealing uses a complete stochastic approach. background 0.71 0.09 0.04 asserted 1.00
s4 The performance of each technique is investigated using a structural optimisation problem. method 0.95 0.90 0.02 asserted 0.98
s5 These performances are then compared to each other as and to a steepest descent (SD) method. method 0.92 0.88 0.05 asserted 0.95

What kind of paper the classifier reports this is: simulation study 0.52 · evidence from simulation or computation 0.96.

What was examined, and what was not

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not examined
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  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper presents an investigation of two search techniques, tabu search (TS) and simulated annealing (SA), to assess their relative merits when applied to engineering design optimisation. Design optimisation problems are generally characterised as having multi-modal search spaces and discontinuities making global optimisation techniques beneficial. Both techniques claim to be capable of locating globally optimum solutions on a range of problems but this capability is derived from different underlying philosophies. While tabu search uses a semi-deterministic approach to escape local optima, simulated annealing uses a complete stochastic approach. The performance of each technique is investigated using a structural optimisation problem. These performances are then compared to each other as and to a steepest descent (SD) method.

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