Artificial intelligence
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Classifier systems and genetic algorithms
Artificial Intelligence
Parallel processing: principles and practice
Parallel processing: principles and practice
TREAT: a new and efficient match algorithm for AI production systems
TREAT: a new and efficient match algorithm for AI production systems
Parallelism and programming in classifier systems
Parallelism and programming in classifier systems
GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
The gamma model and its discipline of programming
Science of Computer Programming
Emergent computation
Generalizing the notion of schema in genetic algorithms
Artificial Intelligence
Transaction processing systems
Transaction processing systems
The state of the art in parallel production systems
Journal of Parallel and Distributed Computing
Programming by multiset transformation
Communications of the ACM
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Probabilistic parallel programming based on multiset transformation
Future Generation Computer Systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
A new kind of science
Parallel, Distributed and Multiagent Production Systems
Parallel, Distributed and Multiagent Production Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Architecture of Symbolic Computers
The Architecture of Symbolic Computers
Learning and Programming in Classifier Systems
Machine Learning
Proceedings of the Third International Workshop on Ant Algorithms
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
An Overview of Evolutionary Computation
ECML '93 Proceedings of the European Conference on Machine Learning
Parallel Computing with DNA: Toward the Anti-Universal Machine
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parallel Genetic Algorithms in Optimization
Physik und Informatik - Informatik und Physik, Arbeitsgespräch
Molecular Computing: From Conformational Pattern Recognition to Complex Processing Networks
Selected papers from the German Conference on Bioinformatics
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Cytocomputation in a biologically inspired, dynamically reconfigurable hardware platform
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This paper describes a rule-based multiset programming paradigm, as a unifying theme for biological, chemical, DNA, physical and molecular computations. The computations are interpreted as the outcome arising out of deterministic, nondeterministic or stochastic interaction among elements in a multiset object space which includes the environment. These interactions are like chemical reactions and the evolution of the multiset can mimic the biological evolution. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the elements evolve toward an equilibrium or an emergent state. Hence, this paradigm is widely applicable; e.g., to conventional algorithms, evolutionary algorithms, Markov chain Monte Carlo based Bayesian inference, genetic algorithms, self-organized criticality and active walker models (swarm and ant intelligence), DNA and molecular computing. Practical realisation of this paradigm is achieved through a coordination programming language using Multiset and transactions. This paradigm permits carrying out parts or all of the computations independently on distinct processors and is eminently suitable for cluster and grid computing.