Integrating Evolutionary Computation with Neural Networks

There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique

Design of the Artificial: lessons from the biological roots of general intelligence

Our desire and fascination with intelligent machines dates back to the antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought (syllogism) and Heron of Alexandria's mechanical machines and automata. However, the quest for Artificial General Intelligence (AGI) is troubled with repeated failures of strategies and approaches throughout the history. This decade has seen a shift in interest towards bio-inspired software and hardware, with the assumption that such mimicry entails intelligence. Though these steps are fruitful in certain directions and have advanced automation, their singular design focus renders them highly inefficient in achieving AGI. Which set of requirements have to be met in the design of AGI? What are the limits in the design of the artificial? Here, a careful examination of computation in biological systems hints that evolutionary tinkering of contextual processing of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence

Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

Feature selection for modular GA-based classification

Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, Relative Importance Factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced

Forcing neurocontrollers to exploit sensory symmetry through hard-wired modularity in the game of Cellz

Several attempts have been made in the past to construct encoding schemes that allow modularity to emerge in evolving systems, but success is limited. We believe that in order to create successful and scalable encodings for emerging modularity, we first need to explore the benefits of different types of modularity by hard-wiring these into evolvable systems. In this paper we explore different ways of exploiting sensory symmetry inherent in the agent in the simple game Cellz by evolving symmetrically identical modules. It is concluded that significant increases in both speed of evolution and final fitness can be achieved relative to monolithic controllers. Furthermore, we show that a simple function approximation task that exhibits sensory symmetry can be used as a quick approximate measure of the utility of an encoding scheme for the more complex game-playing task

Modular feature selection using relative importance factors

Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. In this paper, we explore the use of feature selection in modular GA-based classification. We propose a new feature selection technique, Relative Importance Factor (RIF), to find irrelevant features in the feature space of each module. By removing these features, we aim to improve classification accuracy and reduce the dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that RIF can be used to determine irrelevant features and help achieve higher classification accuracy with the feature space dimension reduced. The complexity of the resulting rule sets is also reduced which means the modular classifiers with irrelevant features removed will be able to classify data with a higher throughput

Evolutionary Design of Artificial Neural Networks Using a Descriptive Encoding Language

Automated design of artificial neural networks by evolutionary algorithms (neuroevolution) has generated much recent research both because successful approaches will facilitate wide-spread use of intelligent systems based on neural networks, and because it will shed light on our understanding of how "real" neural networks may have evolved. The main challenge in neuroevolution is that the search space of neural network architectures and their corresponding optimal weights can be high-dimensional and disparate, and therefore evolution may not discover an optimal network even if it exists. In this dissertation, I present a high-level encoding language that can be used to restrict the general search space of neural networks, and implement a problem-independent design system based on this encoding language. I show that this encoding scheme works effectively in 1) describing the search space in which evolution occurs; 2) specifying the initial configuration and evolutionary parameters; and 3) generating the final neural networks resulting from the evolutionary process in a human-readable manner. Evolved networks for ``n-partition problems'' demonstrate that this approach can evolve high-performance network architectures, and show by example that a small parsimony factor in the fitness measure can lead to the emergence of modular networks. Further, this approach is shown to work for encoding recurrent neural networks for a temporal sequence generation problem, and the trade-offs between various recurrent network architectures are systematically compared via multi-objective optimization. Finally, it is shown that this system can be extended to address reinforcement learning problems by evolving architectures and connection weights in a hierarchical manner. Experimental results support the conclusion that hierarchical evolutionary approaches integrated in a system having a high-level descriptive encoding language can be useful in designing modular networks, including those that have recurrent connectivity