Teaching–learning-based genetic algorithm (TLBGA): an improved solution method for continuous optimization problems

2021 ◽  
Author(s):  
Foroogh Behroozi ◽  
Seyed Mohammad Hassan Hosseini ◽  
Shib Sankar Sana
Keyword(s):  
Genetic Algorithm ◽  
Solution Method ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Optimization Problems ◽  
Teaching Learning

Enabling the Extended Compact Genetic Algorithm for Real-Parameter Optimization by Using Adaptive Discretization

2010 ◽  
Vol 18 (2) ◽  
pp. 199-228 ◽  
Author(s):  
Ying-ping Chen ◽  
Chao-Hong Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Economic Dispatch ◽  
Continuous Optimization ◽  
Memetic Algorithms ◽  
Discretization Method ◽  
Adaptive Discretization ◽  
Compact Genetic Algorithm ◽  
Economic Dispatch Problem ◽  
Continuous Optimization Problems

An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

On initial populations of a genetic algorithm for continuous optimization problems

2006 ◽  
Vol 37 (3) ◽  
pp. 405-436 ◽  
Author(s):  
Heikki Maaranen ◽  
Kaisa Miettinen ◽  
Antti Penttinen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Optimization Problems

A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems

2021 ◽  
Author(s):  
Kayvan Asghari ◽  
Mohammad Masdari ◽  
Farhad Soleimanian Gharehchopogh ◽  
Rahim Saneifard
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Gray Wolf ◽  
Continuous Optimization Problems

A Study of the Continuous Optimization Problem Using a Wood Robot Controlled by a Biologically Motivated System

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Jong-Chen Chen
Keyword(s):  
Physical Environment ◽  
Intelligent System ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Continuous Optimization ◽  
Physical Structure ◽  
Experimental Results ◽  
Self Organized ◽  
Selection Operators ◽  
Continuous Optimization Problems

Continuous optimization plays an increasingly significant role in everyday decision-making situations. Our group had previously developed a multilevel system called the artificial neuromolecular system (ANM) that possessed structure richness allowing variation and/or selection operators to act on it in order to generate a broad range of dynamic behaviors. In this paper, we used the ANM system to control the motions of a wooden walking robot named Miky. The robot was used to investigate the ANM system's capability to deal with continuous optimization problems through self-organized learning. Evolutionary learning algorithm was used to train the system and generate appropriate control. The experimental results showed that Miky was capable of learning in a continued manner in a physical environment. A further experiment was conducted by making some changes to Miky's physical structure in order to observe the system's capability to deal with the change. Detailed analysis of the experimental results showed that Miky responded to the change by appropriately adjusting its leg movements in space and time. The results showed that the ANM system possessed continuous optimization capability in coping with the change. Our findings from the empirical experiments might provide us another dimension of information of how to design an intelligent system comparatively friendlier than the traditional systems in assisting humans to walk.

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