Kenneth De Jong

George Mason University | GMU · Department of Computer Science

Animals ranging from rats to humans can demonstrate cognitive map capabilities. We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary algorithm to replicate the behavior and neural activity observed in rats during a spatial and working memory task in a triple T-maze. The rat was simulated in the Webots...

To date, efforts to automatically configure problem representations for classes of optimization problems have yielded few practical results. We show that a recently proposed approach for training neural G-P maps for optimization problems yields maps that generalize poorly to translated problem instances. We propose that alternative neural architect...

We introduce a genetic programming method for solving multiple Boolean circuit synthesis tasks simultaneously. This allows us to solve a set of elementary logic functions twice as easily as with a direct, single-task approach.

We introduce a genetic programming method for solving multiple Boolean circuit synthesis tasks simultaneously. This allows us to solve a set of elementary logic functions twice as easily as with a direct, single-task approach.

Stochastic search is often the only viable option to address complex optimization problems. Recently, evolutionary algorithms have been shown to handle challenging continuous optimization problems related to protein structure modeling. Building on recent work in our laboratories, we propose an evolutionary algorithm for efficiently mapping the mult...

Here we present a framework for the automatic tuning of spiking neural networks (SNNs) that utilizes an evolutionary algorithm featuring indirect encoding to achieve a drastic reduction in the dimensionality of the parameter space, combined with a GPU-accelerated SNN simulator that results in a considerable decrease in the time needed for fitness e...

When combined with machine learning, the black-box analysis of fitness landscapes promises to provide us with easy-to-compute features that can be used to select and configure an algorithm that is well-suited to the task at hand. As applications that involve computationally expensive, stochastic simulations become increasingly relevant in practice,...

Background Structural excursions of a protein at equilibrium are key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, bu...

A number of papers have emerged in the last two years that apply and study asynchronous master-slave evolutionary algorithms based on a steady-state model. These efforts are largely motivated by the observation that, unlike traditional (synchronous) EAs, asynchronous EAs are able to make maximal use of many parallel processors, even when some indiv...

Recent work in computational structural biology focuses on modeling intrinsically dynamic proteins important to human biology and health. The energy landscapes of these proteins are rich in minima that correspond to alternative structures with which a dynamic protein binds to molecular partners in the cell. On such landscapes, evolutionary algorith...

Understanding function regulation in proteins that switch between different structural states at equilibrium requires both finding the basins that correspond to such states and computing the sequence of intermediate structures employed (i.e., the path taken) in basin-to-basin switching. Recent worksuggests that evolutionary strategies can be used t...

The sense of which individuals have had a major impact on one's life sharpens as one gets older. In my case there is no doubt that John Holland has had the strongest influence on my academic and professional life. As a graduate student in Computer Science at the University of Michigan in the late 1960s, I enrolled in Holland's Adaptive Systems cour...

The aim of this study was to examine the role of a software tool in diagnosing student's thinking during problem solving in mathematics with 41 college students. Students were asked to select relevant steps, facts and strategies represented on the screen and connect them by arrows, indicating their plan of solution. Only after the diagram was compl...

Interest in co-evolutionary algorithms was triggered in part with Hillis 1991 paper describing his success in using one to evolve sorting networks. Since then there have been heightened expectations for using this nature-inspired technique to improve on the range and power of evolutionary algorithms for solving difficult computation problems. Howev...

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms...

The focus on important diseases of our time has prompted many experimental labs to resolve and deposit functional structures of disease-causing or disease-participating proteins. At this point, many functional structures of wildtype and disease-involved variants of a protein exist in structural databases. The objective for computational approaches...

Parallelization of fitness evaluation is an established practice in evolutionary computation, and is a necessity in applications where fitness functions are computationally expensive. Traditional master-slave EAs based on a synchronous, generational model incur idle time when there is variance in the time it takes for individuals to have their fitn...

In the last two decades, great progress has been made in molecular modeling through computational treatments of biological molecules grounded in evolutionary search techniques. Evolutionary algorithms (EAs) are gaining popularity beyond exploring the relationship between sequence and function in biomolecules. In particular, recent work is showing t...

Many proteins involved in human proteinopathies exhibit complex energy landscapes with multiple thermodynamically-stable and semi-stable structural states. Landscape reconstruction is crucial to understanding functional modulations, but one is confronted with the multiple minima problem. While traditionally the objective for evolutionary algorithms...

Protein function is the result of a complex yet precise relationship between protein structure and dynamics. The ability of a protein to assume different structural states is key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to complement experimental techniques in obtaining a comprehensive and...

In many applications of evolutionary algorithms, the time required to evaluate the fitness of individuals is long and variable. When the variance in individual evaluation times is non-negligible, traditional, synchronous master-slave EAs incur idle time in CPU resources. An asynchronous approach to parallelization of EAs promises to eliminate idle...

Background: Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection...

A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known "curse of dimensionality" problem. Recently, the field of time series classification has seen success by usin...

In the last two decades, great progress has been made in molecular modelling through computational treatments of biological molecules grounded in evolutionary search techniques. Evolutionary search algorithms (EAs) are gaining popularity beyond exploring the relationship between sequence and function in biomolecules. In particular, recent work is s...

The problem of computationally determining a protein's native structure from amino acid sequence along remains a central challenge in computational structural biology. Ab-initio protein structure prediction is typically posed as an optimization problem where conformations corresponding to a protein's native structure are those with low potential en...

The scalability of machine learning (ML) algorithms has become a key issue as the size of training datasets continues to increase. To address this issue in a reasonably general way, a parallel boosting algorithm has been developed that combines concepts from spatially structured evolutionary algorithms (SSEAs) and ML boosting techniques. To get mor...

The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships bet...

MASON is an open source multiagent simulation library geared towards simulating very large numbers of relatively lightweight interacting agents. MASON has been used for a wide variety of simulation tasks in robotics, the social sciences, biology, and animation. On June 15 and 16, 2013, approximately two dozen invitees convened at George Mason Unive...

MASON is an open source multiagent simulation library geared towards simulating very large numbers of relatively lightweight interacting agents. MASON has been used for a wide variety of simulation tasks in robotics, the social sciences, biology, and animation. On June 15 and 16, 2013, approximately two dozen invitees convened at George Mason Univ...

Artificial Neural Networks (ANNs) as well as Support Vector Machines (SVMs) are very powerful tools which can be utilized for remote sensing classification. This paper exemplifies the applicability of ANNs and SVMs in land cover classification. A brief introduction to ANNs and SVMs were given. The ANN and SVM methods for land cover classification u...

The scalability of machine learning (ML) algorithms has become increasingly important due to the ever increasing size of datasets and increasing complexity of the models induced. Standard approaches for dealing with this issue generally involve developing parallel and distributed versions of the ML algorithms and/or reducing the dataset sizes via s...

Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a matu...

Earthquakes and many other natural disasters occur in Japan. The rapid delivery of aid supplies and the transportation of the injured are important for alleviating human suffering caused by disasters. This problem can be represented by the Multi-Depot Vehicle Routing Problem (MDVRP). In this paper, the method of solving the Multi-Depot Vehicle Rout...

The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary Algorithms unclear. Originally, it was coined to put a group of stochastic search algorithms that mimic natural evolution together. While some people would still see it as a specific term devoted to this group of algorit...

Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternati...

Recently Quantitative Genetics has been successfully employed to understand and improve operators in some Evolutionary Algorithms (EAs) implementations. This theory offers a phenotypic view of an algorithm's behavior at a population level, and suggests new ways of quantifying and measuring concepts such as exploration and exploitation. In this pape...

Associating functional information with biological sequences remains a challenge for machine learning methods. The performance of these methods often depends on deriving predictive features from the sequences sought to be classified. Feature generation is a difficult problem, as the connection between the sequence features and the sought property i...

GMU-BICA, the biologically-inspired self-aware cognitive architecture developed at George Mason University, continues to be a useful prototype for various intelligent artifacts, including intelligent tutoring systems, yet the underlying formalism of mental states used in its design was never described in detail. The present theoretical work aims at...

People have been inventing and tinkering with various forms of evolutionary algorithms (EAs) since the 1950s when digital computers became more readily available to scientists and engineers. Today we see a wide variety of EAs and an impressive array of applications. This diversity is both a blessing and a curse. It serves as strong evidence for the...

The annotation of DNA regions that regulate gene transcription is the first step towards understanding phenotypical differences among cells and many diseases. Hypersensitive (HS) sites are reliable markers of regulatory regions. Mapping HS sites is the focus of many statistical learning techniques that employ Support Vector Machines (SVM) to classi...

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Prediction of promoter regions continues to be a challenging subproblem in mapping out eukaryotic DNA. While this task is key to understanding the regulation of differen- tial transcription, the gene-specific architecture of promoter sequences does not readily lend itself to general strategies. To date, the best approaches are based on Support Vect...

Segmentation of satellite images is an important step for the success of the object detection and recognition in image processing. Segmentation is the process of dividing the image into disjoint homogeneous regions. There are many segmentation methods and approaches, the most popular are clustering methods and approaches such as Fuzzy C-Means (FCM)...

Hypersensitive (HS) sites in genomic sequences are reliable markers of DNA regulatory regions that control gene expression. Annotation of regulatory regions is important in understanding phenotypical differences among cells and diseases linked to pathologies in protein expression. Several computational techniques are devoted to mapping out regulato...

Customizing and evolutionary algorithm (EA) for a new or unusual problem can seem relatively simple as long as one can devise an appropriate representation and reproductive operators to modify it. Unfortunately getting a customized EA to produce high quality results in a reasonable amount of time can be quite challenging. There is little guidance a...

The field of Evolutionary Computation has experienced tremendous growth over the past 25 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships bet...

This paper proposes a method to improve the recognition of regulatory genomic sequences. Annotating sequences that regulate gene transcription is an emerging challenge in genomics research. Identifying regulatory sequences promises to reveal underlying reasons for phenotypic differences among cells and for diseases associated with pathologies in pr...

Support vector machines (SVMs) are now one of the most popular machine learning techniques for solving difficult classification problems. Their effectiveness depends on two critical design decisions: 1) mapping a decision problem into an n-dimensional feature space, and 2) choosing a kernel function that maps the n-dimensional feature space into a...

The field of Evolutionary Computation has experienced tremendous growth over the past 40 years, resulting in a wide variety of evolutionary algorithms and applications. This poses two interesting challenges: to provide a unified view of the field and to identify important open issues. This talk addresses these challenges by giving an overview of a...

GMU BICA is a biologically inspired cognitive architecture developed at George Mason University. Its main distinguishing feature is a system of data structures called “mental states” that enables various forms of metacognition. The present study develops an understanding of the role of metacognition during working scenario generation (a general ele...

Presents an interview with Kenneth A. De Jong, a professor at George Mason University.

The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships bet...

Bloat is a common problem with Evolutionary Algorithms (EAs) that use variable length representation. By creating unnecessarily large individuals it results in longer EA runtimes and solutions that are difficult to interpret. The causes of bloat are still uncertain, but one theory suggests that it occurs when the phenotype (e.g. behaviors) of the p...

This article provides a brief overview of the field of Evolutionary Computation. It describes the important historical developments that shaped the field. It summarizes the field as it exists today and discusses some of the important directions in which the field is developing. Copyright © 2009 John Wiley & Sons, Inc. For further resources related...

Applying evolutionary algorithms to traditional single-objective optimization problems is now a well-understood and successful process. Less well understood but developing nicely is the use of evolutionary algorithms for multi-objective optimization problems. Least well understood is the use of coevolutionary algorithms to solve co-optimization pro...

Identification of vulnerabilities of water distribution systems and identification of appropriate counter-measures are important components of homeland security. These are difficult and time consuming tasks. This paper provides a new approach to resolve these problems in complex infrastructure systems. It is based on the use of co-evolutionary comp...

Identifying patterns of factors associated with aircraft accidents is of high interest to the aviation safety community. However, accident data is not large enough to allow a significant discovery of repeating patterns of the factors. We applied the STUCCO algorithm to analyze aircraft accident data in contrast to the aircraft incident data in majo...

The field of Evolutionary Computation has experienced tremendous growth over the past 25 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships bet...

Already in the early 1960s, Larry Fogel and his colleagues were exploring the possibility of creating artificial intelligence using simulated evolution. Over the past 50 years, that idea has captured the imagination of many people and has led to a wide variety of approaches. In this article, this quest is summarized, the current state of the art is...

Significant progress can be made in the part of elementary school education that relies on intelligent tutoring systems (ITS), if the role of a referee and a peer advisor will be performed by a pedagogical agent that is a computer implementation of a cognitive architecture modeling the process of learning. Recent studies in cognitive architectures...

Implementation of agency in a cognitive system implies that certain beliefs, values and/or goals represented in the system become, if implicitly, attributed to the self of the agent. When the cognitive system becomes explicitly aware of this attribution, it acquires a self-regulation capacity allowing it to control, modify and develop its self-conc...

Our theoretical understanding of island models (IMs) is much worse than of single-population evolutionary algorithms (EAs). As a consequence there is relatively little guidance available to a practitioner for even the most basic aspects of IM design such as choosing the size and number of the islands. In this paper we improve on this situation by s...

Parameterized evolutionary algorithms (EAs) have been a standard part of the Evolutionary Computation community from its inception. The widespread use and applicability of EAs is due in part to the ability to adapt an EA to a particular problem-solving context by tuning its parameters. However, tuning EA parameters can itself be a challenging task...

In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: n-point crossover and uniform crossover. We then show empirically that disruption analysis alone is n...

There continues to be a growing interest in the use of co-evolutionary algorithms to solve difficult computational problems. However, their performance has varied widely from good to disappointing. The main reason for this is that co-evolutionary systems can display quite complex dynamics. Therefore, in order to efficiently use co-evolutionary algo...

Cooperative coevolution is often used to solve difficult opti- mization problems by means of problem decomposition. Its performance on this task is influenced by many design de- cisions. It would be useful to have some knowledge of the performance effects of these decisions, in order to make the more beneficial ones. In this paper we study the effe...

The field of AI is now more than 30 years old and has produced a variety of impressive intelligent systems as well as some striking failures. As we continue to raise our goals and expectations, it becomes increasingly clear that simple, single methodology approaches are inadequate. However, the design and implementation of complex, multifaceted sys...

The task of designing cognitive tests and challenges for robots and intelligent agents is vital for guidance and evaluation of the development of cognitive architectures aimed at the human level of intelligence. In addressing this task, the tremendous store of knowledge of experimental psychology cannot be ignored. At the same time, methods and tes...

The goal of this research is to explore the effects of social interactions between individual autonomous vehicles (AVs) in various problem scenarios. We take a look at one way to construct the social relationships and generate data from computer simulations to compare the behaviors of each. A difference can be noticed when synthetic social structur...

There continues to be a growing interest in the use of coevolutionary algorithms (CoEAs) to solve difficult computational problems. In particular, cooperative CoEAs are often used for optimization by means of problem decomposition. In addition to the parameters of traditional evolutionary algorithms (EAs), CoEAs have a set of coevolution specific p...

As artificial intelligence approaches the human level, the task of designing cognitive tests and challenges for robots becomes critical for clarifying and reproducing elements of the "magic" of human cognition. In solving this task, the store of knowledge of experimental psychology cannot be ignored. At the same time, methods and test paradigms use...

Designing a robot control system that is able to intelligently decide how to handle prioritizing and combining the actions from multiple, conflicting goals is necessary for effective au-tonomous behavior. The method proposed in this paper uses a Genetic Algorithm (GA) to evolve the relative goal weights that enable agents to combine outputs from co...

The challenge addressed by this research project is to create a hybrid cognitive architecture that will possess key features of human higher cognition. Our approach is based on the integration of symbolic and connectionist components at the top representational level. The framework of schemas, which is the base of this architecture, is described in...

: This paper introduces an integrated research and design support tool, called Emergent Designer, developed at George Mason University. It is a tool that implements models of various complex systems, including cellular automata and evolutionary algorithms, to represent engineering systems and their related design processes. The system is intended f...

Co-evolutionary algorithms (CEAs) have been applied to optimization and machine learning problems with often mediocre results. One of the causes for the unfulfilled expectations is the discrepancy between the external problem solving goal and the internal mechanisms of the algorithm. In this paper, we investigate in a principled way the relationshi...

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason Universit...

Several researchers have used Price's equation (from biology theory literature) to analyze the various components of an Evolutionary Algorithm (EA) while it is running, giving insights into the components contributions and interactions. While their results are interesting, they are also limited by the fact that Price's equation was designed to work...