Abstract: Evolutionary algorithms (EAs) show strong adaptability in solving the complex optimization problems. Fitness evaluation is an important step of EA. In this step, the fitness of individuals ...

Abstract: Robust multiobjective optimization problems (RMOPs) widely exist in real-world applications, which introduce a variety of uncertainty in optimization models. While some evolutionary ...

Over the past decade, Professor L. Mahadevan's Soft Math Lab at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has helped establish how the ancient Japanese paper arts ...

Awards recognize foundational research that helped shape mixed-integer and nonlinear optimization—and reflect the company’s ...

Awards recognize foundational research that helped shape the fields of mixed-integer and nonlinear optimization-and reflect the company's deep scientific roots. Gurobi Optimization, LLC, the leader ...

Multi-objective optimization problems (MOPs) demand algorithms that effectively balance convergence, diversity, and computational efficiency. To address this challenge, a novel Multi-Objective Human ...

Reactor radiation-shielding optimization methods are proposed by combining many-objective evolutionary algorithms with particle-transport calculation software that can optimize the reactor ...

Science and engineering problems in real world includes multiple conflicting objectives required to be optimized and are called multi-objective optimization Problems (MooPs). The aim is to minimize or ...

Dr. James McCaffrey of Microsoft Research uses full code samples to detail an evolutionary algorithm technique that apparently hasn't been published before. The goal of a combinatorial optimization ...

Dr. James McCaffrey of Microsoft Research says that when quantum computing becomes generally available, evolutionary algorithms for training huge neural networks could become a very important and ...