Heuristic Search Strategies for Noisy Optimization

2020 ◽  
pp. 356-370
Author(s):  
Manuel Dalcastagné
Keyword(s):  
Heuristic Search ◽  
Search Strategies ◽  
Noisy Optimization

scholarly journals Heuristic search strategies for multiobjective state space search

Sadhana ◽  
1996 ◽  
Vol 21 (3) ◽  
pp. 263-290
Author(s):  
Pallab Dasgupta ◽  
P P Chakrabarti ◽  
S C Desarkar
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Search Strategies ◽  
State Space Search

scholarly journals Choice of Exhaustive or Heuristic Search to Solve AI Problems

2019 ◽  
Vol 34 (2) ◽  
pp. 91-104
Author(s):  
Dalya A. Gatsh
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Heuristic Search ◽  
Search Strategy ◽  
Search Strategies ◽  
Human Beings ◽  
Memory Space ◽  
Search Mechanism ◽  
The Common ◽  
Living Environments

 Artificial intelligence is an appealing area of research in computer science because it is concerned with the discovering of effective techniques that have been mainly motivated from human beings or their living environments to solve problems that have special nature. In this research, we aim first to introduce and analyze the common characteristics of problems that artificial intelligence interested in, and then we will highlight how to prepare such problems to solve them by search. The main goal of our study is helping us to decide which search strategy is better through investigating the behavior of most popular search strategies to find out the desired solution for two examples of a simple artificial intelligence problem. Our experiments presented that the required time and memory space to solve the problem mainly affected by many factors such as the applied search mechanism, the solution position, the number of available solutions, and the complexity in search.

Phylogeny of the Rhabdocoela (Platyhelminthes): a working hypothesis

1993 ◽  
Vol 71 (2) ◽  
pp. 298-308 ◽  
Author(s):  
Ulf Jondelius ◽  
Mikael Thollesson
Keyword(s):  
Heuristic Search ◽  
Common Ancestor ◽  
Ultrastructural Level ◽  
Search Strategies ◽  
Last Common Ancestor ◽  
Parsimony Analysis ◽  
Working Hypothesis ◽  
Parasitic Flatworm ◽  
The Impact

An attempt is made to resolve contradictions in recent studies of the phylogeny of the Rhabdocoela (including the major parasitic flatworm groups), with a hypothesis based on parsimony analysis of characters determined at both the light microscopic and the ultrastructural level. The rhabdocoel subtaxa "Dalyellioida" and "Typhloplanoida" are shown to be non-monophyletic. The taxon Endoaxonemata, comprising the last common ancestor of the Neodermata, Pterastericolidae, Fecampiidae, and Acholadidae and all its descendants is named as a result of the study. Crustaceans are proposed as the primitive hosts of this group. Separate islands of equally parsimonious trees were encountered in some of the parsimony analyses performed. Failure to find all such islands may cause misinterpretation of the results. The impact on the results of different methods of rooting cladograms and different heuristic search strategies implemented in the PAUP software are discussed.

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