Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring often comes at the cost accuracy. We provide formal tools this work for reconciling fundamental tension algorithm fairness. Specifically, we put use concept Pareto optimality from multiobjective optimization seek fairness-accuracy front a neural network classifier. demonstr...

Location and types of sensors may be integrated for simultaneous achievement of water security goals and other water utility objectives, such as regulatory monitoring requirements. Complying with the recent recommendations on dual benefits of sensors, this study addresses the optimal location of these types of sensors in a multipurpose approach. The study presents two mathematical models for ...

A core challenge ofMultiobjective Evolutionary Algorithms (MOEAs) is to attain evenly distributed Pareto optimal solutions along the Pareto front. In this paper, we propose a novel asymmetric Pareto-adaptive (apa) scheme for the identification of well distributed Pareto optimal solutions based on the geometrical characteristics of the Pareto front. The apa scheme applies to problem with symmetr...

Dealing with multi-objective problems by using generation methods has some interesting advantages since it provides the decision-maker complete information about set of non-dominated points (Pareto front) and a clear overview problem. However, providing many solutions to might also be overwhelming. As an alternative approach, presenting representative Pareto front may advantageous. Choosing suc...

A multi-objective heuristic particle swarm optimiser (MOHPSO) based on Pareto multi-objective theory is proposed to solve multi-objective optimality problems. The optimality objectives are the roof displacement and structure weight. Two types of structure are analysed in this paper, a truss structure and a framework structure. Performance-based seismic analysis, such as classical and modal push...

We give an efficient algorithm to enumerate all elements of a Pareto front in a multi-objective optimization problem in which the space of values is finite for all objectives. Our algorithm uses a feasibility check for a search space element as an oracle and minimizes the number of oracle calls that are necessary to identify the Pareto front of the problem. Given a k-dimensional search space in...

In this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods which are used as the main tools for comparing and ranking solutions, an auxiliary comparison approach called fuzzy possession is ...

Multi-objective AI planning suffers from a lack of benchmarks with known Pareto Fronts. A tunable benchmark generator is proposed, together with a specific solver that provably computes the true Pareto Front of the resulting instances. A wide range of Pareto Front shapes of various difficulty can be obtained by varying the parameters of the generator. The experimental performances of an actual ...