We develop a rule-based optimization approach. The rules are IF-THEN statements that allow domain experts to model their complex systems in a natural framework. We formalize the enterprise problem as minimizing multi-criteria objectives with respect to a set of business rules. We construct a finite state machine in which the rules correspond to state transitions. The states are evaluated by an ...

Support Vector Machines are models widely used in supervised classification. The classical model minimizes a compromise between the structural risk and empirical risk. In this paper, we consider Machine with feature selection design implement bi-objective evolutionary algorithm for approximating Pareto optimal frontier of two objectives. metaheuristic is based on non-dominated sorting genetic i...

Many designers concede that there is typically more than one measure of performance for an artifact. Often, a large system is decomposed into smaller subsystems each having its own set of objectives, constraints, and parameters. The performance of the final design is a function of the performances of the individual subsystems. It then becomes necessary to consider the tradeoffs that occur in a ...

Many real-world applications are characterized by multiple conflicting objectives. In such problems optimality is replaced by Pareto optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, achieving an accurate representation of the Pareto frontier is still an imp...

This paper presents the methodology used for finding the optimal set of foot trajectories for a quadruped robot using manual tuning and multiobjective genetic algorithm optimization that provide energy efficient and fast locomotion. Manual trajectory tuning is used to obtain initial set of trajectories for the multiobjective GA optimization. The multiobjective optimization evaluates the energy ...

Marcet and Marimon (1994, revised 1998) developed a recursive saddle point method which can be used to solve dynamic contracting problems that include participation, enforcement and incentive constraints. Their method uses a recursive multiplier to capture implicit prior promises to the agent(s) that were made in order to satisfy earlier instances of these constraints. As a result, their method...

Simple and complex contracts Negotiation protocols work, in general, via the iterative exchange of proposals and counter-proposals. An agent starts with a contract that’s optimal for that agent and makes concessions, in each subsequent proposal, until either an agreement is reached or the negotiation is abandoned because the latest proposal’s utility has fallen below the agents’ reservation val...

We consider a decision-making problem to evaluate absolute ratings of alternatives from the results their pairwise comparisons according two criteria, subject constraints on ratings. formulate as bi-objective optimization constrained matrix approximation in Chebyshev sense logarithmic scale. The is approximate comparison matrices for each criterion simultaneously by common consistent unit rank,...

In bilateral Negotiation Analysis, the literature often considers the case of complete information. In this context, since the negotiators know the value functions of both parties, it is not difficult to calculate the Pareto frontier and the Pareto efficient solutions for the negotiation. Thus rational negotiators can reach agreement on this frontier. However, these approaches are not applied i...

The Hyper-Space Diagonal Counting (HSDC) method was previously proposed for generating representations of n-dimensional data in 2or 3-dimensions. Since its inception, the HSDC has been used to visualize the n-dimensional performance space in an intuitive fashion for Multiobjective Optimization Applications, through the incorporation of the HSDC into the Hyperspace Pareto Frontier (HPF) visualiz...