Robert G. Reynolds

Wayne State University | WSU · Department of Computer Science

The goal of this paper is to investigate the applicability of evolutionary algorithms to the design of real-time industrial controllers. Present-day “deep learning” (DL) is firmly established as a useful tool for addressing many practical problems. This has spurred the development of neural architecture search (NAS) methods in order to automate the...

One of the major challenges facing Artificial Intelligence in the future is the design of trustworthy algorithms. The development of trustworthy algorithms will be a key challenge in Artificial Intelligence for years to come. Cultural Algorithms (CAs) are viewed as one framework that can be employed to produce a trustable evolutionary algorithm. Th...

Present-day 'deep learning' (DL) is firmly established as a useful tool for addressing many practical problems. This has spurred the development of neural architecture search (NAS) methods in order automate the model search activity. CATNeuro is a NAS algorithm based on the graph evolution concept devised by Neuroevolution of Augmenting Topologies...

In Cultural Systems, there are many ways to collect and distribute problem‐solving knowledge within social networks. Such mechanisms include games, auctions, and various voting mechanisms. Here, a new auction mechanism, Common Value Auctions, is presented. In this paper, Common Value Auctions are used to distribute problem‐solving knowledge within...

Cultural Algorithms provide a metaheuristic framework for the application of various solution mechanism represented by Knowledge Sources in the Belief Space to the evolution of complex social networks in the population space. One of the key components in the configuration of a Cultural Algorithm is the knowledge distribution mechanism, and how the...

The structure of contemporary AI applications in complex automation domains, such as robotics and autonomous driving, is multistaged and hierarchical. The overall pipeline consists of perception, planning, and actuation subsystems. Each of these in turn consists of staged processing. Such systems consume raw sensor data and process it to respond in...

Cultural Algorithms are a knowledge‐intensive framework that is based on how human cultural systems adjust their structures and contents to address changes in their environments. While there is wide variety of ways in which the Cultural Algorithms can be implemented, there is a general metaphor that describes the learning process in all of them. Th...

Cultural Algorithms (CA) are population-based stochastic optimization methods that are modelled after human culture and are suited to solving problems in complex environments. The CA Belief Space stores harvested knowledge from prior generations and redistributes it to future generations via a knowledge distribution (KD) mechanism. The population o...

Contemporary ‘deep learning’ (DL) models are proven effective. However, the network topologies may be complex and not immediately obvious for the problem at hand. This has given rise to the secondary field of neural architecture search (NAS). This paper describes a NAS method based on graph evolution pioneered by Neuroevolution of Augmenting Topolo...

Abstract-In Cultural Systems there are many ways to collect and distribute problem solving knowledge within social networks. Such mechanisms include games, auctions, and various voting mechanisms. Here, a new distribution mechanism, Common Value Auctions, is presented. In this paper Common Value Auctions are used to distribute problem solving knowl...

The notion of collaborative scientific workflow is coined to address the increasing need for collaborative data analytics using the scientific workflow paradigm. In collaborative environments, access control policies are necessary for controlling the sharing of workflows, data products, and provenance information among collaborating parties. In par...

Workflow scheduling has remained a critical functionality of modern data-centric workflow management systems. Cloud computing, which provides practically unlimited computing and storage resources, has enabled a new generation of data-centric workflows, called big data workflows. New big data workflow scheduling algorithms should optimally utilize t...

In Cultural Systems there are many ways to collect and distribute problem solving knowledge within social networks. Such mechanisms include games, auctions, and various voting mechanisms. Here, a new auction mechanism, Common Value Auctions, is presented. In this paper Common Value Auctions are used to distribute problem solving knowledge within a...

In Cultural Systems there are many ways to collect and distribute problem solving knowledge within social networks. Such mechanisms include games, auctions, and various voting mechanisms. Here, a new auction mechanism, Common Value Auctions, is presented. In this paper Common Value Auctions are used to distribute problem solving knowledge within a...

In this chapter, the author shows how Cultural Algorithms offer data-driven problem solving in complex systems.

In this chapter the author examines lineage-based subculture social organization performance.

In this chapter, the author presents the Cultural Engine, showing how to put individuals to work.

In this chapter, the author shows how to model the social fabric.

In this chapter, the author compares the nuclear family and extended family organizations.

In this chapter, the author presents the Cultural Algorithm framework, including knowledge sources, the Communication Protocol, and the Population Space.

This chapter explains social metrics.

This chapter concludes the book.

This chapter examines chaos, in particular the Cones World Generator.

Over the last few decades, a plethora of improved evolutionary algorithms was developed with exquisite performance on numerical and real-world problems. Among such algorithms, the Cultural Algorithm is a hyper-heuristic evolutionary algorithm, which explicitly utilizes the knowledge represented in the belief space as an essential component to guide...

Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of different optimization problems with complex characteristics. In this paper, an improved class of...

Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes...

Many real-world problems can be formulated as optimization problems. Such problems pose a challenge for researchers in the design of efficient algorithms capable of finding the best solution with the least computational cost. In this paper, a new evolutionary algorithm is proposed that combines the explorative and exploitative capabilities of two e...

Numerous different methodologies have been introduced in the last few decades to provide efficient solutions for complex real-world problems and other optimization problems. This work focuses on the development of a simple hybrid cultural learning theme with a balanced performance for differential evolution frameworks. It is intended to be always e...

It has been conjectured that “Culture is an optimizing process”. That is, the social structures that emerge in a given organization support the efficient solution of problems that the organization must deal with. Here we will present an escalating sequence of static problems to our system. Our goal will be to study how the social network infrastruc...

In recent years, Cultural Algorithms (CAs) have attracted substantial research interest. When applied to highly multimodal and high dimensional problems, Cultural Algorithms suffer from fast convergence followed by stagnation. This research proposes a novel hybridization between Cultural Algorithms and a modified multiple trajectory search (MTS). I...

This paper introduces a Cluster-based Differential Evolution Algorithm with Heterogeneous Influence for solving complex optimization problems. The idea behind this combination is to classify the Differential Evolution population into a number of clusters using k-means clustering method and to apply different mutation strategies for the clusters. Th...

Many meta-heuristics methods are applied to guide the exploration and exploitation of the search space for large scale optimization problems. These problems have attracted much attention from researchers who proposed developed a variety of techniques for locating the optimal solutions. Cultural Algorithm has been recently adopted to solve global nu...

With the increasing availability of gene sets and pathway resources, novel approaches that combine both resources to reconstruct networks from gene sets are of interest. Currently, few computational approaches explore the search space of candidate networks using a parallel search. In particular, search agents employed by evolutionary computational...

In previous work, we investigated the performance of Cultural Algorithms (CA) over the complete range of system complexities in a benchmarked environment. In this paper the goal is to discover whether there is a similar internal process going on in CA problem solving, regardless of the complexity of the problem. We are to monitor the 'vital signs'...

Significance Some of the most pivotal questions in human history necessitate the investigation of archaeological sites that are now under water. These contexts have unique potentials for preserving ancient sites without disturbance from later human occupation. The Alpena-Amberley Ridge beneath modern Lake Huron in the Great Lakes offers unique evid...

While caribou hunting structures are well known in the circumpolar region, equivalent features are difficult to investigate further south due to significant changes in sea level and subsequent human activity. The discovery of hunting structures on submerged landforms beneath modern Lake Huron provides a new window into caribou hunting in the mid-la...

This paper presents a new method to solve complex engineering optimization problems. This method is based on intermingling Cultural Algorithms with Tabu search meta-heuristic. The proposed approach uses a specified set of knowledge sources to update agent’s information, as well as employs the diffusion factor to propagate the best solutions in the...

In this research we present a configurable novel framework based on an enhanced heterogeneous hierarchical social fabric influence function embedded in Cultural Algorithms, as a powerful vehicle for the solution of complex problems. We motivate the discussion by investigating the extent to which these emergent phenomena are also visible within nove...

Simulated robotic soccer is frequently used as a test method for contemporary artificial intelligence research. It provides a real-time environment with complex dynamics and sensor information that is both noisy and limited. Team coordination between the robots is essential for success. Cultural Algorithm (CA) is a branch of evolutionary algorithms...

Program understanding plays a very important role in the software engineering field as an essential part of the widely accepted reuse process. Unfortunately, the lack of general tools for this purpose often prevents users from effectively retrieving software modules for reuse. The primary difficulty is in determining which of the already-available...

In this paper we investigate the emergence and power of a complex social system based upon principles of cultural evolution. Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. Here we extend the influence and integration function in Cultural Algorithms by adding a mechanism by...

Within a cultural context we constantly deal effectively with multiple objectives. A computational version of cultural systems, Cultural Algorithms, has been extended to deal with multi-objective optimization problems. These approaches while employing the basic framework have used only a subset of the available knowledge sources. In this paper we p...

The L-CAPS system is a functional interface for learning group behavior using Cultural Algorithms in the land bridge simulation example. It enables the Cultural Algorithms process to implicitly communicate with, modify and evaluate, autonomous game agents restricted to an external virtual world. The L-CAPS system is described and used to learn the...

This chapter investigates the use of agile program design techniques within an online game development laboratory setting. The proposed game concerns the prediction of early Paleo-Indian hunting sites in ancient North America along a now submerged land bridge that extended between Canada and the United States across what is now Lake Huron. While th...

Purpose – The purpose of this paper is to investigate the performance of cultural algorithms (CAs) over a complete range of optimization problem complexities, from fixed to chaotic and specifically observing whether there is a given homogeneous agent topology within a culture which can dominate across all complexities. Design/methodology/approach...

Our goal in this paper was to investigate the impact that the addition of a Heterogeneous layered Social network will have on problem solving ability of a Cultural system. The synergisms of the emergent swarms in the population and belief spaces are affected by training the social network on a dynamic but recurring pattern weaved by our social infl...

Multi-objective optimization is a widely applicable technique in Engineering and Computer Science. In the past, Cultural Algorithms have been used to solve complex optimization and design problems with success. In this paper, we extend the Cultural Algorithm Framework to handle multi-objective problems. The resultant system, Multi-Objective Cultura...

Robustness is a key concern when developing a successful commercial evolutionary tool. In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic. In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the...

Previous multi-agent simulation studies have shown striking differences in the success of different ways of distributing foraged resources (sharing) in bands of pre-linguistic foragers. These early studies were made within a modeled environment that was patchy and with limited carrying capacity. In this paper we investigate the effects of environme...

Our previous work on real-valued function and Engineering Optimization problems had shown that cultural learning emerged as the result of a more focused meta-level interaction or swarming of knowledge sources, “Knowledge Swarms” in the belief space. These meta-level swarms induced the swarming of individuals in a formed social network in the popula...

Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. Here, a single influence function that is designed to integrate together the diverse knowledge sources in order to coordinate their problem solving efforts in an optimization problem. The influ- ence function currently has two...

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. While many successful real-world applications of Cultural Algorithms have been produced, we are intere...

Recently there has been increased interest in socially motivated approaches to problem solving. These approaches include particle swarm optimization, ant colony optimization, and cultural algorithms. Each of these approaches is derived from a social system that operates on potentially different scale. In previous work we introduced a toolkit to mod...

Applying artificial intelligence and data-mining tools to existing archaeological data from Monte Alb´n, a prehistoric urban center, offers the potential for building agent-based models of emergent ancient urban centers. The authors use decision trees to characterize location decisions made by early inhabitants and inject these rules into a sociall...

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. Here, the authors develop an algorithm based on an analogy to the marginal value theorem in foraging t...

Numerous models of modern and ancient urban landscapes have been proposed. While is of interest to classify examples of early urban centers, it is even more interesting to model their origins. Since these emergent centers can be viewed not only as adaptations to their social and biological environments, but also as a source of further change. Thus,...

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. While many successful real- world applications of cultural algorithms have been produced, we are inter...

In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship, over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced recipro...

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. Here, we develop an algorithm based upon an analogy to the marginal value theorem in foraging theory t...

Biggerstaff and Richter suggest that there are four fundamental subtasks associated with operationalizing the reuse process [1]: finding reusable components, understanding these components, modifying these components, and composing components. Each of these subproblems can be re-expressed as a knowledge acquisition problem relative to producing a n...

The 2005 articles by Stoltman et al. and Flannery et al. to which Neff et al. (this issue) have responded are not an indictment of instrumental neutron activation analysis (INAA) but, rather, of the way Blomster et al. (2005) misuse it and of the hyperbolic culture-historical claims they have made from their INAA results. It has long been acknowled...

This paper looks at optimization problem solving from the standpoint of a predator/prey paradigm. In that paradigm, knowledge sources (or decision makers) control the placement of individuals onto a multi-dimensional landscape. Their score is the sum of the resources collected by each of the individuals that they control. While simple, this game ha...

In this paper, we use principles from game theory, computer gaming, and evolutionary computation to produce a framework for investigating one of the great mysteries of the ancient Americas: why did the pre-Hispanic Pueblo (Anasazi) peoples leave large portions of their territories in the late A.D. 1200s? The gaming concept is overlaid on a large-sc...

Our previous work on real-valued function optimization problems had shown that cultural learning emerged as the result of meta-level interaction or swarming of knowledge sources, “knowledge swarms,” in the belief space. These meta-level swarms induced the swarming of individuals in the population space, “Cultural Swarms.” The interaction of these k...

The village multi-agent simulation relieves the years of early Pueblo Indian settlers from A.D. 600 to 1300. The objective is to investigate why these settlers abandoned the region. Initial work modeled environmental aspects, such as water and paleoproductivity data, and farming practices of the households or agents. A cultural algorithm is then im...

Petrographic analysis of Formative Mexican ceramics by J. B. Stoltman et al. (see the companion piece in this issue of PNAS) refutes a recent model of Olmec “one-way” trade. In this paper, we address the model's more fundamental problems of sampling bias, anthropological implausibility, and logical non sequiturs. No bridging argument exists to link...

A research, aimed at unravelling the archaeological structures of the American Southwest using computer modeling, is described. Led by researchers at Massachusetts Institute of Technology, this effort looked at the relations between variables such as total population and the rate of construction of Mayan monuments. Object oriented programming langu...

In this paper we take a multi-agent model of agricultural subsistence in the Mesa Verde region between 600 A.D. and 1300 A.D. and allow the emergence of a set of overlaid social networks over time in response to environmental dynamics. These overlaid networks include kinship, economic, and community level (hub) networks. Agents are able to particip...

In previous work it was observed that certain problem-solving phases emerged during the optimization process for a real-valued functional surface formed within a cones-world environment using a Cultural Algorithm. The Cultural Algorithm was configured using five knowledge sources in the belief space, and an Evolutionary Programming model for the po...

In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship , over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced recipr...

Various biologically inspired approaches to problem solving using a social metaphor have been proposed. For example, both Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) have been employed to solve problems in optimization and design. Both approaches employ simple social interactions between agents to produce emergent social str...

The multi-agent village simulation was initially developed to examine the settlement and farming practices of prehispanic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado (Kohler, 2000; Kohler et al.). The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispanic Pue...

The multi-agent Village simulation was initially developed to examine the settlement and farming practices of prehispanic Pueblo Indians of the Central Mesa Verde region of Southwest Colorado [1,2]. The original model of Kohler was used to examine whether drought alone was responsible for the departure of the prehispanic Puebloan people from the Fo...

Many languages are in serious danger of being lost and if nothing is done to prevent it, half of the world's approximately 6,500 languages will disappear in the next 100 years. Language data are central to the research of a large social science community, including linguists, anthropologists, archeologists, historians, sociologists, and political s...

Previous work on real-valued function optimization problems had shown that cultural learning emerged as the result of meta-level interaction or swarming of knowledge sources, "knowledge swarms" in the belief space. These meta-level swarms induced the swarming of individuals in the population space, "cultural swarms". The interaction of these knowle...

The initial version of the model used in this study, Village 1.0, was implemented by Tim Kohler and a team of developers mostly from Washington State University. The original model addressed environmental constraints only and did not attempt to model social interaction. In a recent paper we employed Cultural Algorithms as a framework in which to ad...