A New Genetic-Based Approach for Function Optimization

2014 ◽  
Vol 1006-1007 ◽  
pp. 1051-1056
Author(s):  
Azam Rabiee ◽  
Masoumeh Vali
Keyword(s):  
Genetic Algorithm ◽  
Population Based ◽  
Continuous Functions ◽  
Function Optimization ◽  
Test Problems ◽  
Initial Population ◽  
New Mutation ◽  
Simple Bounds ◽  
Conventional Genetic Algorithm ◽  
Bounded Continuous Functions

We present a novel genetic-based algorithm for optimizing n-D simple-bounded continuous functions. In this paper, we propose a new mutation operator, called rotational mutation. The proposed approach starts from the vertices of the polytope created by the simple bounds, as the initial population. Similar to the conventional genetic algorithm, we calculate the optimum point of each population based on its cost value using the elitism mechanism. Then, we create the new generations based on the proposed rotational mutation and the conventional crossover operators. We have evaluated the algorithm on the two well-known test problems. Experimental results showed that the proposed approach outperforms the conventional genetic algorithm, in terms of the number of generations.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

A Species Conserving Genetic Algorithm for Multimodal Function Optimization

2002 ◽  
Vol 10 (3) ◽  
pp. 207-234 ◽  
Author(s):  
Jian-Ping Li ◽  
Marton E. Balazs ◽  
Geoffrey T. Parks ◽  
P. John Clarkson
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multiple Solutions ◽  
Species Conservation ◽  
Function Optimization ◽  
Test Problems ◽  
Next Generation ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
A New Technique

This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

scholarly journals Self-Adaptive Artificial Bee Colony for Function Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhu Tang ◽  
Wen Long ◽  
Huawei Wu ◽  
Kang Zhang ◽  
Yuri A. W. Shardt
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Initial Population ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Global And Local ◽  
Self Adaptive

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

AN AIDED GENETIC ALGORITHM FOR MULTIPROCESSOR SCHEDULING

1999 ◽  
Vol 09 (03) ◽  
pp. 423-436 ◽  
Author(s):  
V. VAIDEHI ◽  
C. N. KRISHNAN ◽  
P. SWAMINATHAN
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Multiprocessor Scheduling ◽  
Initial Population ◽  
Task Graph ◽  
Filtering Algorithm ◽  
Schedule Length ◽  
Local Extrema ◽  
Conventional Genetic Algorithm ◽  
Random Nature

Genetic algorithms have been used for solving the problem of scheduling the tasks represented by a task graph onto parallel computing architectures to minimize the schedule length of the task graph. Due to the random nature of the initial population they however face the local extrema problem which could make the resulting schedules sub-optimal. To minimize this problem, an Aided Genetic Algorithm(AGA) is proposed in this paper, in which a member of the initial population of the Genetic algorithm is obtained from a heuristic pre-scheduler. It is found that the AGA achieves the required convergence in (a) lesser number of iterations, and (b) lesser number of trials in obtaining the near-optimal solution compared to the conventional genetic algorithm. The proposed AGA also takes the inter-task communication into account while scheduling. The method is then applied to the problem of optimally scheduling the Kalman filtering algorithm onto a multi-transputer network. The results are experimentally on a network of T-805 transputers.

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