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Evolutionary Neural Architecture Search (ENAS) automates convolutional neural network (CNNs) construction. Cooperative Coevolution (COCE) divides an optimisation problem into sub-problems. Our COCE ENAS algorithm decomposes a CNN into sub-architectures, ...

Cooperative coevolutionary algorithms prove effective in solving tasks that can be easily decoupled into subproblems. When applied to problems with high coupling (where the fitness depends heavily on specific joint actions), evolution is often stifled by ...

Data transparency is beneficial to data participants’ awareness, users’ fairness, and research work’s reproducibility. However, when addressing transparency requirements, we cannot ignore data privacy. This article defines the multi-objective data ...

Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint ...

Reinforcement learning (RL) is experiencing a resurgence in research interest, where Learning Classifier Systems (LCSs) have been applied for many years. However, traditional Michigan approaches tend to evolve large rule bases that are difficult to ...

The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause ...

In many multiagent domains, and particularly in tightly coupled domains, teasing an agent's contribution to the system performance based on a single episodic return is difficult. This well-known difficulty hits state-to-action mapping approaches such as ...

The traditional evolutionary instance selection algorithm has the risk of redundant and noise training samples in the training set selection, which affects the classification effect. In this paper, instance selection, instance weighting and feature ...

Large-scale global optimization (LSGO) problems are known as hard problems for many evolutionary algorithms (EAs). LSGO problems are usually computationally costly, thus an experimental analysis for choosing an appropriate algorithm and its parameter ...

For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered an effective strategy to decompose the problem into smaller subproblems, each of which can then be solved individually. Among these decomposition methods, ...

Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning RL, increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is being ...

Cooperative co-evolution (CC) is a powerful evolutionary computation framework for solving large scale global optimization (LSGO) problems via the strategy of "divide-and-conquer", but its efficiency highly relies on the decomposition result. Existing ...

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-...

By taking the idea of divide-and-conquer, cooperative coevolution (CC) provides a powerful architecture for large scale global optimization (LSGO) problems, but its efficiency highly relies on the decomposition strategy. It has been shown that ...

In recent years, research on large scale global optimization (LSGO) provided metaheuristics able to effectively tackle real-valued objective functions depending on thousand of variables. Nevertheless, finding a suitable solution of LSGO problems often ...

Recently we introduced an approach to solving Sudoku problems with co-evolution [4]: Resource-defined Fitness Sharing for Sudoku (RFSS). The idea is to find a set of non-conflicting numerals such that every cell in the puzzle is "covered" by a numeral. ...

By taking the idea of divide-and-conquer, cooperative coevolution provides a powerful architecture for large scale optimization problems, but its efficiency depends heavily on the decomposition strategy. Existing decomposition algorithms either cannot ...

In this paper we show the potentiality of Embodied Evolution in the optimization of general multi-robot systems, as compared to state-of-the-art approaches based on Cooperative Coevolution. The comparison is carried out in a real application problem of ...

Conflict avoidance plays a crucial role in guaranteeing the safety and efficiency of the air traffic management system. Recently, the strategic conflict avoidance SCA problem has attracted more and more attention. Taking into consideration the large-...