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Simultaneous parallel search for multiple members of an evolutionary algorithm can lead to effective optimization.
The results showed that for both individual and pooled data the participants tended to use random search strategies thereby validating the random search model for multiple targets.
We present experimental results that show that genetic search using a multiple representation – Gray-coded mutation and binary-coded crossover – outperforms search using just one representation.
A cellular-decomposition-based framework for cooperative, adaptive search is proposed that allows multiple search platforms to adapt to changes in both mission objectives and environmental parameters.
Proceedings ArticleDOI
Jeff Huang, Thomas Lin, Ryen W. White 
08 Feb 2012
28 Citations
Both branching and backtracking are viable methods for visiting multiple search results.
As a motivating example, this paper improves biotechnology methods to do associative search in DNA databases.
Multiple runs of the algorithm can further improve the search result.
Proceedings ArticleDOI
Gang Luo, Chunqiang Tang 
01 Dec 2008
Due to searcherspsila limited medical knowledge and the taskpsilas inherent difficulty, searchers often cannot find desired search results in a single pass and have to search iteratively for multiple passes.
Proceedings ArticleDOI
T. Schnier, Xin Yao 
16 Jul 2000
31 Citations
This paper proposes and studies multiple representations in an evolutionary algorithm and shows empirically how multiple representations can benefit searches as much as a good search operator could.
The technique is both generalizable to multiple search agents and extensible in that additional real-life search requirements (maneuverability constraints, additional information about the sensor, etc.)
This algorithm is perfect for multiple pattern search.
In this paper, we propose a new way of combining multiple evolutionary algorithms, each of which may run with multiple search operators.
We demonstrate the effectiveness of the proposed search method in multiple scenarios with varying numbers of agents.
Open accessJournal ArticleDOI
Eduard Ort, Christian N. L. Olivers 
08 Jun 2020-Visual Cognition
16 Citations
A better understanding of multiple-target search will also contribute to better design of real-life multiple-target search problems, reducing the risk of detrimental search failures.
We believe other search methods that use multiple search heuristics may also benefit from using multiple representations, one tuned for each heuristic.
The study provides evidence that consideration of multiple search strategies would enhance the design of search output structures.
Using this approach, data preparation can be easily accomplished in Excel.

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