In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.

/pdf/evolving-fuzzy-rules-for-relaxed-criteria-negotiation-3crdvmhhfc.pdf

Evolving Fuzzy Rules for Relaxed-Criteria Negotiation

WSN in cyber physical systems: Enhanced energy management routing approach using software agents

We introduce some standard types of combinatorial optimization problems, and indicate ways in which one might attack them using Differential Evolution. Our main focus will be on indexing by relative position (also known as order based representation); we will describe some related approaches as well. The types of problems we will consider, which are abstractions of ones from engineering, go by names such as knapsack problems, set coverings, set partitioning, and permutation assignment. These are historically significant types of problems, as they show up frequently, in various guises, in engineering and elsewhere. We will see that a modest amount of programming, coupled with a sound implementation of Differential Evolution optimization, can lead to good results within reasonable computation time.We will also show how Differential Evolution might be hybridized with other methods from combinatorial optimization, in order to obtain better results than might be found with the individual methods alone.

Relative Position Indexing Approach

Minimizing electromagnetic pollution and power consumption in green heterogeneous small cell network deployment

This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem.
Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. We evaluate the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches is made in two forms: firstly a handcrafted approach compares GAs without repair against those using GeneRepair. A second automated approach is then presented. This meta-genetic algorithm examines different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search.
Results have identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions for 50, 70 and 100 city problems. GeneRepair assists in finding TSP solutions in an extremely efficient manner, in both time and number of evaluations required.

/pdf/evolutionary-computation-applied-to-combinatorial-35mj20afu5.pdf

Evolutionary computation applied to combinatorial optimisation problems

In this paper we present the outcome of two recent sets of experiments to evaluate the effectiveness of a new adjunct genetic operator GeneRepair This operator was developed to correct invlaid tours which may be generated following crossover or mutation of our particular implementation of the genetic algorithm Following implementation and testing of our genetic algotihm with GeneRepair we found a significant positive side in our results Using GeneRepair along side traditional corsover and mutation operators we have been able to travers the search space of a problem and generate very good results in an extremely efficent manner, in both time and number of evaluations required

/pdf/generepair-a-repair-operator-for-genetic-algorithms-4got6a8ktq.pdf

GeneRepair - A Repair Operator for Genetic Algorithms

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GeneRepair - A Repair Operator for Genetic Algorithms