Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
PADO: a new learning architecture for object recognition
Symbolic visual learning
Evolving programmers: the co-evolution of intelligent recombination operators
Advances in genetic programming
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
PADO: Learning Tree Structured Algorithms for Orchestration into an Object Recognition System
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
Adaptation technique for integrating genetic programming and reinforcement learning for real robots
IEEE Transactions on Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Stock trading strategies by genetic network programming with flag nodes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Varying portfolio construction of stocks using genetic network programming with control nodes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A portfolio optimization model using Genetic Network Programming with control nodes
Expert Systems with Applications: An International Journal
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Ranking association rules for classification based on genetic network programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A genetic network programming with learning approach for enhanced stock trading model
Expert Systems with Applications: An International Journal
Digital ecosystems: stability of evolving agent populations
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Genetic network programming with reconstructed individuals
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-car elevator group supervisory control system using genetic network programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constructing portfolio investment strategy based on time adapting genetic network programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic network programming for fuzzy association rule-based classification
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Generalized time related sequential association rule mining and traffic prediction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic network programming with rule accumulation considering judgment order
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Evolving plural programs by genetic network programming with multi-start nodes
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A model of portfolio optimization using time adapting genetic network programming
Computers and Operations Research
A method of association rule analysis for incomplete database using genetic network programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Genetic relation algorithm with guided mutation for the large-scale portfolio optimization
Expert Systems with Applications: An International Journal
Efficient program generation by evolving graph structures with multi-start nodes
Applied Soft Computing
A novel evolutionary method to search interesting association rules by keywords
Expert Systems with Applications: An International Journal
Use of infeasible individuals in probabilistic model building genetic network programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Searching interesting association rules based on evolutionary computation
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
An evolutionary approach to rank class association rules with feedback mechanism
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Parse-matrix evolution for symbolic regression
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Natural Computing: an international journal
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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.