A note on continuous search algorithms

1987 ◽  
Vol 24 (1) ◽  
pp. 277-280
Author(s):  
L. E. Garey ◽  
R. D. Gupta
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Search Methods ◽  
Average Complexity

Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.

A note on continuous search algorithms

1987 ◽  
Vol 24 (01) ◽  
pp. 277-280
Author(s):  
L. E. Garey ◽  
R. D. Gupta
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Search Methods ◽  
Average Complexity

Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.

Random Search Methods

2010 ◽  
pp. 55-66 ◽  
Author(s):  
William W. Tipton ◽  
Richard G. Hennig
Keyword(s):  
Random Search ◽  
Search Methods

Solving Machine Loading Problem of FMS

2011 ◽  
pp. 19-43 ◽  
Author(s):  
Anoop Prakash ◽  
Nagesh Shukla ◽  
Ravi Shankar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Random Search ◽  
Search Algorithms ◽  
Ant Colony ◽  
Complex Nature ◽  
Machine Loading ◽  
Loading Problem

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

Random Search Methods

2006 ◽  
pp. 2175-2180
Author(s):  
H. Edwin Romeijn
Keyword(s):  
Random Search ◽  
Search Methods

Stopping rules in k-adaptive global random search algorithms

2010 ◽  
Vol 48 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Anatoly Zhigljavsky ◽  
Emily Hamilton
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Stopping Rules ◽  
Global Random Search
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