90 research outputs found
Evolutionary design of deep neural networks
Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of
the topology of artificial neural networks, with most works focusing on very simple architectures.
However, times have changed, and nowadays convolutional neural networks are the industry and
academia standard for solving a variety of problems, many of which remained unsolved before the
discovery of this kind of networks.
Convolutional neural networks involve complex topologies, and the manual design of these
topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to
use neuroevolution in order to evolve the architecture of convolutional neural networks.
To do so, we have decided to try two different techniques: genetic algorithms and grammatical
evolution. We have implemented a niching scheme for preserving the genetic diversity, in order
to ease the construction of ensembles of neural networks. These techniques have been validated
against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%,
and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275.
Both results have proven very competitive when compared with the state of the art. Also, in all
cases, ensembles have proven to perform better than individual models.
Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced
in 2017, which includes more samples and a set of letters for character recognition. Results have
shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures
can be reused across domains with similar characteristics.
In summary, neuroevolution is an effective approach for automatically designing topologies for
convolutional neural networks. However, it still remains as an unexplored field due to hardware
limitations. Current advances, however, should constitute the fuel that empowers the emergence of
this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917.
This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca
A Neurogenetic Algorithm Based on Rational Agents
Lately, a lot of research has been conducted on the automatic design of artificial neural networks (ADANNs) using evolutionary algorithms, in the so-called neuro-evolutive algorithms (NEAs). Many of the presented proposals are not biologically inspired and are not able to generate modular, hierarchical and recurrent neural structures, such as those often found in living beings capable of solving intricate survival problems. Bearing in mind the idea that a nervous system's design and organization is a constructive process carried out by genetic information encoded in DNA, this paper proposes a biologically inspired NEA that evolves ANNs using these ideas as computational design techniques. In order to do this, we propose a Lindenmayer System with memory that implements the principles of organization, modularity, repetition (multiple use of the same sub-structure), hierarchy (recursive composition of sub-structures), minimizing the scalability problem of other methods. In our method, the basic neural codification is integrated to a genetic algorithm (GA) that implements the constructive approach found in the evolutionary process, making it closest to biological processes. Thus, the proposed method is a decision-making (DM) process, the fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. In other words, the penalty approach implemented through the fitness function automatically rewards the economical ANNs with stronger generalization and extrapolation capacities. Our method was initially tested on a simple, but non-trivial, XOR problem. We also submit our method to two other problems of increasing complexity: time series prediction that represents consumer price index and prediction of the effect of a new drug on breast cancer. In most cases, our NEA outperformed the other methods, delivering the most accurate classification. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems
Efficient Evolution of Neural Networks
This thesis addresses the study of evolutionary methods for the synthesis of neural network controllers. Chapter 1 introduces the research area, reviews the state of the art, discusses promising research directions, and presents the two major scientific objectives of the thesis. The first objective, which is covered in Chapter 2, is to verify the efficacy of some of the most promising neuro-evolutionary methods proposed in the literature, including two new methods that I elaborated. This has been made by designing extended version of the double-pole balancing problem, which can be used to more properly benchmark alternative algorithms, by studying the effect of critical parameters, and by conducting several series of comparative experiments. The obtained results indicate that some methods perform better with respect to all the considered criteria, i.e. performance, robustness to environmental variations and capability to scale-up to more complex problems. The second objective, which is targeted in Chapter 3, consists in the design of a new hybrid algorithm that combines evolution and learning by demonstration. The combination of these two processes is appealing since it potentially allows the adaptive agent to exploit a richer training feedback constituted by both a scalar performance objective (reinforcement signal or fitness measure) and a detailed description of a suitable behaviour (demonstration). The proposed method has been successfully evaluated on two qualitatively different robotic problems. Chapter 4 summarizes the results obtained and describes the major contributions of the thesis
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Evolutionary algorithms (EA), a class of stochastic search methods based on
the principles of natural evolution, have received widespread acclaim for their
exceptional performance in various real-world optimization problems. While
researchers worldwide have proposed a wide variety of EAs, certain limitations
remain, such as slow convergence speed and poor generalization capabilities.
Consequently, numerous scholars actively explore improvements to algorithmic
structures, operators, search patterns, etc., to enhance their optimization
performance. Reinforcement learning (RL) integrated as a component in the EA
framework has demonstrated superior performance in recent years. This paper
presents a comprehensive survey on integrating reinforcement learning into the
evolutionary algorithm, referred to as reinforcement learning-assisted
evolutionary algorithm (RL-EA). We begin with the conceptual outlines of
reinforcement learning and the evolutionary algorithm. We then provide a
taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the
RL-assisted strategy adopted by RL-EA, and its applications according to the
existing literature. The RL-assisted procedure is divided according to the
implemented functions including solution generation, learnable objective
function, algorithm/operator/sub-population selection, parameter adaptation,
and other strategies. Finally, we analyze potential directions for future
research. This survey serves as a rich resource for researchers interested in
RL-EA as it overviews the current state-of-the-art and highlights the
associated challenges. By leveraging this survey, readers can swiftly gain
insights into RL-EA to develop efficient algorithms, thereby fostering further
advancements in this emerging field.Comment: 26 pages, 16 figure
A sequential handwriting recognition model based on a dynamically configurable CRNN
Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods
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Discovering multi-purpose modules through deep multitask learning
Machine learning scientists aim to discover techniques that can be applied across diverse sets of problems. Such techniques need to exploit regularities that are shared across tasks. This begs the question: What shared regularity is not yet being exploited? Complex tasks may share structure that is difficult for humans to discover. The goal of deep multitask learning is to discover and exploit this structure automatically by training a joint model across tasks. To this end, this dissertation introduces a deep multitask learning framework for collecting generic functional modules that are used in different ways to solve different problems. Within this framework, a progression of systems is developed based on assembling shared modules into task models and leveraging the complementary advantages of gradient descent and evolutionary optimization. In experiments, these systems confirm that modular sharing improves performance across a range of application areas, including general video game playing, computer vision, natural language processing, and genomics; yielding state-of-the-art results in several cases. The conclusion is that multi-purpose modules discovered by deep multitask learning can exceed those developed by humans in performance and generality.Computer Science
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Tackling Credit Assignment Using Memory and Multilevel Optimization for Multiagent Reinforcement Learning
There is growing commercial interest in the use of multiagent systems in real world applications. Some examples include inventory management in warehouses, smart homes, planetary exploration, search and rescue, air-traffic management and autonomous transportation systems. However, multiagent coordination is an extremely challenging problem. First, information relevant for coordination is often distributed across the team members, and fragmented amongst each agent's observation histories (past states). Second, the coordination objective is often sparse and noisy from the perspective of an agent. Designing general mechanisms of generating agent-specific reward functions that incentivizes an agent to collaborate towards the shared global objective is extremely difficult. From a learning perspective, both difficulties can be linked to the difficulty of credit assignment - the process of accurately associating rewards with actions.
The primary contribution of this dissertation is to tackle credit assignment in multiagent systems in order to enable better multiagent coordination. First we leverage memory as a tool in enabling better credit assignment by facilitating associations between rewards and actions separated across time. We achieve this by introducing Modular Memory Units (MMU), a memory-augmented neural architecture that can reliably retain and propagate information over an extended period of time. We then use MMU to augment individual agents' policies in solving dynamic tasks that require adaptive behavior from a distributed multiagent team. We also introduce Distributed MMU (DMMU) which uses memory as a shared knowledge base across a team of distributed agents to enable distributed one-shot decision making.
Switching our attention from the agent to the learning algorithm, we then introduce Evolutionary Reinforcement Learning (ERL), a multilevel optimization framework that blends the strength of policy gradients and evolutionary algorithms to improve learning. We further extend the ERL framework to introduce Collaborative ERL (CERL) which employs a collection of policy gradient learners (portfolio), each optimizing over varying resolution of the same underlying task. This leads to a diverse set of policies that are able to reach diverse regions within the solution space. Results in a range of continuous control benchmarks demonstrate that ERL and CERL significantly outperform their composite learners while remaining overall more sample-efficient.
Finally, we introduce Multiagent ERL (MERL), a hybrid algorithm that leverages the multilevel optimization framework of ERL to enable improved multiagent coordination without requiring explicit alignment between local and global reward functions. MERL uses fast, policy-gradient based learning for each agent by utilizing their dense local rewards. Concurrently, evolution is used to recruit agents into a team by directly optimizing the sparser global objective. Experiments in multiagent coordination benchmarks demonstrate that MERL's integrated approach significantly outperforms the state-of-the-art multiagent policy-gradient algorithms
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