Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Multithreaded Algorithms for Pricing a Class of Complex Options
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
ARA - The Ant-Colony Based Routing Algorithm for MANETs
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
Ant Colony Optimization
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Option pricing using Particle Swarm Optimization
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Ant colony optimization to price exotic options
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm optimization algorithm for option pricing: extended abstract
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Pricing algorithms for financial derivatives
Algorithms and theory of computation handbook
The Journal of Supercomputing
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Computing option prices is a challenging problem. Finding the best time to exercise an option is a even more challenging problem. One has to be watchful for the price changes in the market place and act at the right time. That is, prices need to be policed. This paper proposes a novel idea for pricing options using a nature inspired meta-heuristic algorithm, Ant Colony Optimization (ACO). ACO has been used extensively in combinatorial optimization problems and recently in dynamic applications such as mobile ad-hoc networks. Specifically, we adapt the general ACO algorithm to apply to a totally different application, computational finance, in the current study. We police the prices using ants to decide on the best time to exercise so that the holder of the option contract will get the maximum benefit out of his/her investment. Our algorithm and implementation suggests a better way to price options than traditional numerical techniques such as binomial lattice algorithm. From our results we conclude that reactive ants may be best suited for long-dated options whose performance can still be improved.