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
Genetic programming in C++: implementation issues
Advances in genetic programming
Information Systems Research
Computational & Mathematical Organization Theory
Unintended consequences of computer-mediated communications
Behaviour & Information Technology
Location Strategies and Knowledge Spillovers
Management Science
Industry Level Supplier-Driven IT Spillovers
Management Science
Opportunism by cheating and its effects on industry profitability. The CIOPS model
Computational & Mathematical Organization Theory
Robustness of centrality measures under uncertainty: Examining the role of network topology
Computational & Mathematical Organization Theory
Agent-based and multi-agent simulations: coming of age or in search of an identity?
Computational & Mathematical Organization Theory
Computational & Mathematical Organization Theory
Interleaving multi-agent systems and social networks for organized adaptation
Computational & Mathematical Organization Theory
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This paper offers an analysis of cluster formations on planer cells comprised of multi-agents utilizing local interactions and state transitions based on Genetic Programming (GP) and its applications. First, we illustrate that if the states of agents are allowed to have continuous values, equilibrium is attained on the basis of the fixed-point theorem. We also show that if the agents are restricted to binary states, equilibrium is attained in an asymptotic sense. However, for agents characterized by more than one state, the attainment of equilibrium is not ensured. We examine our results by using a simulation wherein agents learn from past experiences based on GP. Finally, we demonstrate a system comprised of cluster formations on planer cells comprised of artificial agents, and apply this system to the clustering of employees in firms.