A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks

With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

Selection methods regulate evolution of cooperation in digital evolution

A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations' average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.

Efficient evaluation functions for evolving coordination

Abstract This paper presents fitness evaluation functions that efficiently evolve coordination in large multi-component systems. In particular, we focus on evolving distributed control policies that are applicable to dynamic and stochastic environments. While it is appealing to evolve such policies directly for an entire system, the search space is prohibitively large in most cases to allow such an approach to provide satisfactory results. Instead, we present an approach based on evolving system components individually where each component aims to maximize its own fitness function. Though this approach sidesteps the exploding state space concern, it introduces two new issues: (1) how to create component evaluation functions that are aligned with the global evaluation function; and (2) how to create component evaluation functions that are sensitive to the fitness changes of that component, while remaining relatively insensitive to the fitness changes of other components in the system. If the first issue is not addressed, the resulting system becomes uncoordinated; if the second issue is not addressed, the evolutionary process becomes either slow to converge or worse, incapable of converging to good solutions. This paper shows how to construct evaluation functions that promote coordination by satisfying these two properties. We apply these evaluation functions to the distributed control problem of coordinating multiple rovers to maximize aggregate information collected. We focus on environments that are highly dynamic (changing points of interest), noisy (sensor and actuator faults), and communication limited (both for observation of other rovers and points of interest) forcing the rovers to evolve generalized solutions. On this difficult coordination problem, the control policy evolved using aligned and component-sensitive evaluation functions outperforms global evaluation functions by up to 400%. More notably, the performance improvements increase when the problems become more difficult (larger, noisier, less communication). In addition we provide an analysis of the results by quantifying the two characteristics (alignment and sensitivity discussed above) leading to a systematic study of the presented fitness functions.