An analysis about behavior of evolutionary algorithms: A kind of theoretical description based on global random search methods

1998 ◽  
Vol 3 (1) ◽  
pp. 31-31
Author(s):  
Ding Lixin ◽  
Kang Lishan ◽  
Chen Yupin ◽  
Zhou Shaoquan
Keyword(s):  
Evolutionary Algorithms ◽  
Random Search ◽  
Theoretical Description ◽  
Search Methods ◽  
Global Random Search

scholarly journals Credibilistic fuzzy clustering based on evolutionary method of crazy cats

2021 ◽  
pp. 110-119
Author(s):  
Yevgeniy Bodyanskiy ◽  
Alina Shafronenko ◽  
Iryna Pliss
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Clustering ◽  
High Speed ◽  
Random Search ◽  
Goal Function ◽  
Large Datasets ◽  
Global Extremum ◽  
Mathematical Procedure ◽  
Global Random Search ◽  
Search Modes

The problem of fuzzy clustering of large datasets that are sent for processing in both batch and online modes, based on a credibilistic approach, is considered. To find the global extremum of the credibilistic fuzzy clustering goal function, the modification of the swarm algorithm of crazy cats swarms was introduced, that combined the advantages of evolutionary algorithms and a global random search. It is shown that different search modes are generated by a unified mathematical procedure, some cases of which are known algorithms for both local and global optimizations. The proposed approach is easy to implement and is characterized by the high speed and reliability in problems of multi-extreme fuzzy clustering.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

Random Search Methods

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

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.

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

scholarly journals On the Hardness of Offline Multi-objective Optimization

2007 ◽  
Vol 15 (4) ◽  
pp. 475-491 ◽  
Author(s):  
Olivier Teytaud
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Random Search ◽  
Computational Cost ◽  
Multiobjective Evolutionary Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Insight Into ◽  
Better Than

It has been empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. This paper shows that the convergence rate of all comparison-based multi-objective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search under certain conditions. The number of objectives must be very moderate and the framework should hold the following assumptions: the objectives are conflicting and the computational cost is lower bounded by the number of comparisons is a good model. Our conclusions are: (i) the number of conflicting objectives is relevant (ii) the criteria based on comparisons with random-search for multi-objective optimization is also relevant (iii) having more than 3-objectives optimization is very hard. Furthermore, we provide some insight into cross-over operators.

An Agent-Based Approach to Synthesizing Structures

2015 ◽  
Author(s):  
Robin Kiff ◽  
Matthew Campbell
Keyword(s):  
Random Search ◽  
New Method ◽  
Hill Climbing ◽  
Automated Synthesis ◽  
Search Methods ◽  
Agent Based ◽  
Physics Simulation

This paper discusses a new method for the automated synthesis of structures. By creating a framework to implement the synthesis, several methods are compared for the application of building tall self-supporting towers. These towers are evaluated in a physics simulation and comprised of multiple nodes and connections. In this paper, a new agent-based method is compared to existing search methods including random search, A*, and Hill-Climbing search. With the agents making local changes to nodes in the tower, the method achieves better results with less time and memory.

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