A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms

Comparison of Multi-agent Co-operative Co-evolutionary and Evolutionary Algorithms for Multi-objective Portfolio Optimization

Co-evolutionary techniques makes it possible to apply evolutionary algorithms in the cases when it is not possible to formulate explicit fitness function. In the case of social and economic simulations such techniques provide us tools for modeling interactions between social or economic agents—especially when agent-based models of co-evolution are used. In this paper agent-based versions of mul...

Multi-objective evolutionary algorithms for a preventive healthcare facility network design

Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. In this paper, a bi-objective mathematical model is proposed to design a network of preventive healthcare facilities so as to minimize total travel and waiting time as well as establishment and staffing cost. Moreover, each facility acts as M/M/1 queuin...

Multi-Objective Evolutionary Algorithms

Evolutionary Multi-Objective Algorithms

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary co...

Multi-Objective Evolutionary Algorithms

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...