Several approaches have been developed into evolutionary algorithms to deal with dynamic optimization problems, of which memory and random immigrants are two major schemes. This pa...
This paper proposes a genetic algorithm (GA) with random immigrants for dynamic optimization problems where the worst individual and its neighbours are replaced every generation. I...
Many real-world optimization problems are dynamic in nature. The interest in the Evolutionary Algorithms (EAs) community in applying EA variants to dynamic optimization problems h...
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dynamic optimization problems online, i.e. as time goes by. For a proper design, t...
When facing dynamic optimization problems the goal is no longer to find the extrema, but to track their progression through the space as closely as possible. Over these kind of ov...
Carlos Fernandes, Agostinho C. Rosa, Vitorino Ramo...
In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are t...
— In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolv...
Yang Yan, Hongfeng Wang, Dingwei Wang, Shengxiang ...
— Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present a stigmerg...
Dynamic time-linkage problems (DTPs) are common types of dynamic optimization problems where "decisions that are made now ... may in‡uence the maximum score that can be obta...