Computational Complexity Analysis and Algorithm Design for Combinatorial Optimization Problems

2012 ◽  
Author(s):  
Toshimasa Watanabe
Keyword(s):  
Computational Complexity ◽  
Combinatorial Optimization ◽  
Complexity Analysis ◽  
Optimization Problems ◽  
Algorithm Design ◽  
Combinatorial Optimization Problems

Computational Complexity Analysis of Selective Breeding Algorithm

2014 ◽  
Vol 591 ◽  
pp. 172-175
Author(s):  
M. Chandrasekaran ◽  
P. Sriramya ◽  
B. Parvathavarthini ◽  
M. Saravanamanikandan
Keyword(s):  
Computational Complexity ◽  
Selective Breeding ◽  
Heuristic Algorithms ◽  
Complexity Analysis ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Problem Size ◽  
Combinatorial Optimization Problems ◽  
Complete Problems

In modern years, there has been growing importance in the design, analysis and to resolve extremely complex problems. Because of the complexity of problem variants and the difficult nature of the problems they deal with, it is arguably impracticable in the majority time to build appropriate guarantees about the number of fitness evaluations needed for an algorithm to and an optimal solution. In such situations, heuristic algorithms can solve approximate solutions; however suitable time and space complication take part an important role. In present, all recognized algorithms for NP-complete problems are requiring time that's exponential within the problem size. The acknowledged NP-hardness results imply that for several combinatorial optimization problems there are no efficient algorithms that realize a best resolution, or maybe a close to best resolution, on each instance. The study Computational Complexity Analysis of Selective Breeding algorithm involves both an algorithmic issue and a theoretical challenge and the excellence of a heuristic.

scholarly journals The Computational Challenges of Pursuing Multiple Goals: Network Structure of Goal Systems Predicts Human Performance

2018 ◽  
Author(s):  
Daniel Reichman ◽  
Falk Lieder ◽  
David Bourgin ◽  
Nimrod Talmon ◽  
Thomas L. Griffiths
Keyword(s):  
Computational Complexity ◽  
Human Performance ◽  
Optimization Problems ◽  
Algorithm Design ◽  
Goal Achievement ◽  
Self Control ◽  
Set Cover ◽  
Behavioral Experiments ◽  
Combinatorial Optimization Problems ◽  
Goal Systems

Extant psychological theories attribute people’s failure to achieve their goals primarily to failures of self-control, insufficient motivation, or lacking skills. We develop a complementary theory specifying conditions under which the computational complexity of making the right decisions becomes prohibitive of goal achievement regardless of skill or motivation. We support our theory by predicting human performance from factors determining the computational complexity of selecting the optimal set of means for goal achievement. Following previous theories of goal pursuit, we express the relationship between goals and means as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. This allows us to map two computational challenges that arise in goal achievement onto two classic combinatorial optimization problems: Set Cover and Maximum Coverage. While these problems are believed to be computationally intractable on general networks, their solution can be nevertheless efficiently approximated when the structure of the network resembles a tree. Thus, our initial prediction was that people should perform better with goal systems that are more tree-like. In addition, our theory predicted that people’s performance at selecting means should be a U-shaped function of the average number of goals each means is relevant to and the average number of means through which each goal could be accomplished. Here we report on six behavioral experiments which confirmed these predictions. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to pursue their goals effectively.

scholarly journals COST SHARING IN NETWORKS: SOME OPEN QUESTIONS

2013 ◽  
Vol 15 (02) ◽  
pp. 1340001 ◽  
Author(s):  
HERVÉ MOULIN
Keyword(s):  
Computational Complexity ◽  
Combinatorial Optimization ◽  
Cooperative Game ◽  
Cost Sharing ◽  
Optimization Problems ◽  
Fair Division ◽  
Optimal Solutions ◽  
Combinatorial Optimization Problems ◽  
Open Questions

The fertile application of cooperative game techniques to cost sharing problems on networks has so far concentrated on the Stand Alone core test of fairness and/or stability, and ignored many combinatorial optimization problems where this core can be empty. I submit there is much room for an axiomatic discussion of fair division in the latter problems, where Stand Alone objections are not implementable. But the computational complexity of optimal solutions is still a very severe obstacle to this approach.

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