Techniques of acceleration for association rule induction with pseudo-artificial life algorithm

2010 ◽  
Vol 93 (2) ◽  
pp. 1-11
Author(s):  
Masaaki Kanakubo ◽  
Masafumi Hagiwara
Keyword(s):  
Association Rule ◽  
Artificial Life ◽  
Rule Induction ◽  
Artificial Life Algorithm

Artificial Life for Pavement Distress Survey

2005 ◽  
Vol 295-296 ◽  
pp. 507-514
Author(s):  
Qi Wang ◽  
Hui Zhang
Keyword(s):  
Artificial Life ◽  
Noise Removal ◽  
Automatic Recognition ◽  
Experimental Results ◽  
Road Maintenance ◽  
Pavement Distress ◽  
Artificial Life Algorithm

Automatic recognition of road distresses is of considerable interest since it facilitates road maintenance. We propose the use of artificial life for pavement distress survey. All artificial organisms in the artificial life algorithm exhibit the principle of cooperation which can be found in computer virtues and the hunting process of predators. Experimental results demonstrate that the proposed approach has a strong effect on noise removal such as elimination of oil stains and removal of dark spots which is one of the most difficult problems in pavement distress survey

Pareto Artificial Life Algorithm for Multi-Objective Optimization

2011 ◽  
Vol 4 (2) ◽  
pp. 43-60
Author(s):  
Jin-Dae Song ◽  
Bo-Suk Yang
Keyword(s):  
Objective Function ◽  
Artificial Life ◽  
Pareto Set ◽  
Function Optimization ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Function ◽  
Artificial Life Algorithm ◽  
Single Objective

Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions for the optimum design of the defined journal bearing. By comparing with the optimum solutions of a single objective function, it is confirmed that the single function optimization result is one of the specific cases of Pareto set of optimum solutions.

Pareto Artificial Life Algorithm for Multi-Objective Optimization

2013 ◽  
pp. 100-115
Author(s):  
Jin-Dae Song ◽  
Bo-Suk Yang
Keyword(s):  
Objective Function ◽  
Artificial Life ◽  
Pareto Set ◽  
Function Optimization ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Function ◽  
Artificial Life Algorithm ◽  
Single Objective

Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions for the optimum design of the defined journal bearing. By comparing with the optimum solutions of a single objective function, it is confirmed that the single function optimization result is one of the specific cases of Pareto set of optimum solutions.

Optimum design of short journal bearings by artificial life algorithm

2001 ◽  
Vol 34 (7) ◽  
pp. 427-435 ◽  
Author(s):  
Bo-Suk Yang ◽  
Yun-Hi Lee ◽  
Byeong-Keun Choi ◽  
Hyung-Ja Kim
Keyword(s):  
Optimum Design ◽  
Artificial Life ◽  
Journal Bearings ◽  
Artificial Life Algorithm

scholarly journals AN EFFICIENT APPROACH USING RULE INDUCTION AND ASSOCIATION RULE MINING ALGORITHMS IN DATA MINING

2013 ◽  
pp. 14-18
Author(s):  
KAPIL SHARMA ◽  
SHEVETA VASHISHT
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Work ◽  
Rule Induction ◽  
High Rate ◽  
Training Dataset ◽  
Rule Mining ◽  
Data Set ◽  
Fast Processing

In this research work we use rule induction in data mining to obtain the accurate results with fast processing time. We using decision list induction algorithm to make order and unordered list of rules to coverage of maximum data from the data set. Using induction rule via association rule mining we can generate number of rules for training dataset to achieve accurate result with less error rate. We also use induction rule algorithms like confidence static and Shannon entropy to obtain the high rate of accurate results from the large dataset. This can also improves the traditional algorithms with good result.

Artificial Life Algorithm for Function Optimization

2000 ◽  
Author(s):  
Bo-Suk Yang ◽  
Yun-Hi Lee
Keyword(s):  
Genetic Algorithm ◽  
Artificial Life ◽  
Dynamic Interaction ◽  
Numerical Results ◽  
Function Optimization ◽  
Test Functions ◽  
Immune Genetic Algorithm ◽  
Sensing System ◽  
Multimodal Function ◽  
Artificial Life Algorithm

Abstract This paper presents an enhanced artificial life algorithm for function optimization. As artificial life organisms have a sensing system, they can find the resource they want and metabolize it. The characteristics of artificial life are emergence and dynamic interaction with the environment. In other words, the micro-interaction with each other in the artificial life’s group results in emergent colonization in the whole system. The optimizing ability and convergent characteristics of this proposed algorithm is verified by using three well-known test functions. The numerical results also show that the proposed algorithm is superior to the genetic algorithm and immune genetic algorithm for the multimodal function.

Sign in / Sign up

Export Citation Format

Share Document