CAEP: AN EVOLUTION-BASED TOOL FOR REAL-VALUED FUNCTION OPTIMIZATION USING CULTURAL ALGORITHMS

1998 ◽  
Vol 07 (03) ◽  
pp. 239-291 ◽  
Author(s):  
Chan-Jin Chung ◽  
Robert G. Reynolds
Keyword(s):  
Function Optimization ◽  
Cultural Algorithms ◽  
Performance Improvements ◽  
Minimization Problems ◽  
Adaptive Models ◽  
Cultural Systems ◽  
Self Adaptation ◽  
Acceptance Function ◽  
Population Component ◽  
Self Adaptive

Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process. The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.

KNOWLEDGE-BASED SELF-ADAPTATION IN EVOLUTIONARY SEARCH

2000 ◽  
Vol 14 (01) ◽  
pp. 19-33 ◽  
Author(s):  
CHAN-JIN CHUNG ◽  
ROBERT G. REYNOLDS
Keyword(s):  
Fuzzy Inference ◽  
Population Level ◽  
Cultural Algorithms ◽  
Performance Improvements ◽  
Minimization Problems ◽  
Knowledge Based ◽  
Cultural Framework ◽  
Self Adaptation ◽  
Acceptance Function ◽  
Generalized Knowledge

Self-adaptation has been frequently employed in evolutionary computation. Angeline1 defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.

scholarly journals Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

2001 ◽  
Vol 9 (2) ◽  
pp. 197-221 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Hans-Georg Beyer
Keyword(s):  
Genetic Algorithms ◽  
Essential Feature ◽  
Function Optimization ◽  
Real Parameter ◽  
The Self ◽  
Test Problems ◽  
Adaptive Genetic Algorithms ◽  
Working Principle ◽  
Self Adaptation ◽  
Self Adaptive

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.

A Two-Layer Approach to Developing Self-Adaptive Multi-Agent Systems in Open Environment

2014 ◽  
Vol 6 (1) ◽  
pp. 65-85 ◽  
Author(s):  
Xinjun Mao ◽  
Menggao Dong ◽  
Haibin Zhu
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Systems ◽  
Multi Agent Systems ◽  
Uncertain Environments ◽  
Implementation Framework ◽  
Fine Grain ◽  
Adaptation Mechanisms ◽  
Multi Agent ◽  
Self Adaptation ◽  
Self Adaptive

Development of self-adaptive systems situated in open and uncertain environments is a great challenge in the community of software engineering due to the unpredictability of environment changes and the variety of self-adaptation manners. Explicit specification of expected changes and various self-adaptations at design-time, an approach often adopted by developers, seems ineffective. This paper presents an agent-based approach that combines two-layer self-adaptation mechanisms and reinforcement learning together to support the development and running of self-adaptive systems. The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.

scholarly journals A Comprehensive Technological Survey on the Dependable Self-Management CPS: From Self-Adaptive Architecture to Self-Management Strategies

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1033 ◽  
Author(s):  
Peng Zhou ◽  
Decheng Zuo ◽  
Kun Hou ◽  
Zhan Zhang ◽  
Jian Dong ◽  
...  
Keyword(s):  
Management Strategies ◽  
Critical Issue ◽  
Research Area ◽  
Self Management ◽  
Self Healing ◽  
Human Errors ◽  
Adaptive Architecture ◽  
Comprehensive Survey ◽  
Self Adaptation ◽  
Self Adaptive

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and few surveys have been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human errors and to simplify management, self-management CPS (SCPS) is a wise choice. To achieve dependable self-management, systematic solutions are necessary to verify the design and to guarantee the safety of self-adaptation decisions, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focusing on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means, then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaptation, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaptation, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

scholarly journals A Novel Mating Approach for Genetic Algorithms

2013 ◽  
Vol 21 (2) ◽  
pp. 197-229 ◽  
Author(s):  
Severino F. Galán ◽  
Ole J. Mengshoel ◽  
Rafael Pinter
Keyword(s):  
Genetic Algorithms ◽  
Random Mating ◽  
Adaptive Strategy ◽  
Mating Strategies ◽  
Function Optimization ◽  
The Novel ◽  
Independent Manner ◽  
Novel Approach ◽  
Domain Independent ◽  
Self Adaptive

Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

scholarly journals Self-adaptation of parameters in a learning classifier system ensemble machine

2010 ◽  
Vol 20 (1) ◽  
pp. 157-174 ◽  
Author(s):  
Maciej Troć ◽  
Olgierd Unold
Keyword(s):  
Classifier Systems ◽  
Learning Classifier System ◽  
Classifier System ◽  
System A ◽  
Learning Classifier ◽  
Ensemble Machine Learning ◽  
One Step ◽  
Self Adaptation ◽  
Selection Of ◽  
Self Adaptive

Self-adaptation of parameters in a learning classifier system ensemble machineSelf-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.

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