The Memory Indexing Evolutionary Algorithm for Dynamic Environments

Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments

Lecture Notes in Computer Science - Applications of Evolutinary Computing ◽

10.1007/978-3-540-71805-5_68 ◽

2007 ◽

pp. 617-626 ◽

Cited By ~ 23

Author(s):

Anabela Simões ◽

Ernesto Costa

Keyword(s):

Evolutionary Algorithm ◽

Dynamic Environments ◽

Variable Size

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A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

Evolutionary Computation ◽

10.1162/evco.2007.15.3.369 ◽

2007 ◽

Vol 15 (3) ◽

pp. 369-398 ◽

Cited By ~ 201

Author(s):

Shingo Mabu ◽

Kotaro Hirasawa ◽

Jinglu Hu

Keyword(s):

Reinforcement Learning ◽

Evolutionary Algorithm ◽

Past History ◽

Structural Characteristics ◽

Memory Function ◽

Genetic Network ◽

Dynamic Environments ◽

Network Programming ◽

History Of ◽

Genetic Network Programming

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, “GNP with Reinforcement Learning (GNPRL)” which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

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Evolutionary Algorithm with Diversity-Reference Adaptive Control in Dynamic Environments

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213014500134 ◽

2015 ◽

Vol 24 (01) ◽

pp. 1450013 ◽

Cited By ~ 4

Author(s):

Maury Meirelles Gouvêa ◽

Aluizio F. R. Araújo

Keyword(s):

Adaptive Control ◽

Evolutionary Algorithms ◽

Evolutionary Algorithm ◽

Reference Model ◽

Evolutionary Process ◽

Solution Space ◽

Adaptive Method ◽

Dynamic Environments ◽

Computational Effort ◽

Population Diversity

Evolutionary algorithms (EAs) can be used to find solutions in dynamic environments. In such cases, after a change in the environment, EAs can either be restarted or they can take advantage of previous knowledge to resume the evolutionary process. The second option tends to be faster and demands less computational effort. The preservation or growth of population diversity is one of the strategies used to advance the evolutionary process after modifications to the environment. We propose a new adaptive method to control population diversity based on a model-reference. The EA evolves the population whereas a control strategy, independently, handles the population diversity. Thus, the adaptive EA evolves a population that follows a diversity-reference model. The proposed model, called the Diversity-Reference Adaptive Control Evolutionary Algorithm (DRAC), aims to maintain or increase the population diversity, thus avoiding premature convergence, and assuring exploration of the solution space during the whole evolutionary process. We also propose a diversity models based on the dynamics of heterozygosity of the population, as models to be tracked by the diversity control. The performance of DRAC showed promising results when compared with the standard genetic algorithm and six other adaptive evolutionary algorithms in 14 different experiments with three different types of environments.

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