John Cartlidge

Combating Coevolutionary Disengagement by Reducing Parasite Virulence

While standard evolutionary algorithms employ a static, absolute fitness metric, co-evolutionary algorithms assess individuals by their performance relative to populations of opponents that are themselves evolving. Although this arrangement offers the possibility of avoiding long-standing difficulties such as premature convergence, it suffers from its own unique problems, cycling, over-focusing and disengagement. Here, we introduce a novel technique for dealing with the third and least explored of these problems. Inspired by studies of natural host-parasite systems, we show that disengagement can be avoided by selecting for individuals that exhibit reduced levels of virulence, rather than maximum ability to defeat coevolutionary adversaries. Experiments in both simple and complex domains are used to explain how this counterintuitive approach may be used to improve the success of coevolutionary algorithms.

Learning lessons from the common cold: How reducing parasite virulence improves coevolutionary optimization

Inspired by the virulence of natural parasites, a novel approach is developed to tackle disengagement, a detrimental phenomenon coevolutionary systems sometimes experience. After demonstrating beneficial results in a simple model, minimum comparison sorting networks are coevolved, with results suggesting that moderating parasite virulence can help in practical problem domains.

Caring versus sharing: How to maintain engagement and diversity in coevolving populations

Coevolutionary optimisation suffers from a series of problems that interfere with the progressive escalating arms races that are hoped might solve difficult classes of optimisation problem. Here we explore the extent to which encouraging moderation in one coevolving population (termed parasites) can alleviate the problem of coevolutionary disengagement. Results suggest that, under these conditions, disengagement is avoided through maintaining variation in relative fitness scores. In order to explore whether standard diversity maintenance techniques such as resource sharing could achieve the same effects, we compare moderating virulence with resource sharing in a simple matching game. We demonstrate that moderating parasite virulence differs significantly from resource sharing, and that its tendency to prevent disengagement can also reduce the likelihood of coevolutionary optimisation halting at mediocre stable states.

Autonomous Virulence Adaptation Improves Coevolutionary Optimization

A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVAs efficiency over reduced virulence and the layered Pareto coevolutionary archive.

Unpicking tartan CIAO plots: Understanding irregular coevolutionary cycling

We report results from a series of studies coevolving players for simple Rock-Paper-Scissors games. These results demonstrate that Current Individual versus Ancestral Opponent (CIAO) plots, which have been proposed as a visualization technique for detecting both coevolutionary progress and coevolutionary cycling, suffer from ambiguity with regard to an important but rarely discussed class of cyclic behavior. While regular cycling manifests itself as a characteristic banded plot, irregular cycling produces an irregular tartan pattern that is also consistent with random drift through strategy space. Although this tartan pattern is often reported in the literature on coevolutionary algorithms, it has received little attention or analysis. Here we argue that irregular cycling will tend to be more prevalent than regular cycling, and that it corresponds to a class of coevolutionary scenario that is important both theoretically and in practice. As such, it is desirable that we improve our ability to distinguish its occurrence from that of random drift, and other forms of coevolutionary dynamic.

Correcting a financial brokerage model for cloud computing: closing the window of opportunity for commercialisation

In April 2012, Rogers and Cliff (R&C) demonstrated a theoretical financial brokerage model for cloud computing that is profitable for the broker, offers reduced costs for cloud users, and generates more predictable demand flow for cloud providers. Relatively cheap, long-term reserved instances (RIs) are bulk-purchased by the broker, and then re-packaged and re-sold as monthly options contracts at a price lower than a user can purchase “on-demand” from the provider. Thus, the broker risks exposure on purchase for margin on sales. R&C’s result has generated significant interest in the cloud computing community and is currently the fifth most accessed research paper of all time in the Journal of Cloud Computing: Advances, Systems and Applications.Here, we perform an independent replication of R&C’s brokerage model using CReST, a discrete event simulation platform for cloud computing developed at the University of Bristol. We identify two implementation problems in R&C’s original work: firstly, the broker buys fewer RIs than the model suggests; and secondly, the broker is undercharged for RIs used. We correct R&C’s results accordingly: while broker’s profits are not as high as R&C suggest, the model still supports the theoretical possibility of a profitable brokerage.However, aggressive competition between cloud providers has reduced the cost of cloud services to users and led to the introduction of new secondary markets where users can buy and sell RIs between themselves. This has squeezed the opportunity for an intermediary brokerage. By recalibrating R&C’s model to fit current market conditions, we conclude that the commercial viability of R&C’s brokerage model has been eradicated. The window of opportunity has now closed.

Comparison of Cloud Middleware Protocols and Subscription Network Topologies using CReST, the Cloud Research Simulation Toolkit

We introduce the Cloud Research Simulation Toolkit (CReST), a new cloud computing simulation tool designed to enable cloud providers to research and test their systems before release. We compare CReST with other known cloud simulation tools and demonstrate the utility of CReST by evaluating different distributed middleware protocols and associated subscription network topologies for robustness and reliability. Our results extend previous work and demonstrate that the published literature contains inaccuracies. CReST has been released as open-source under a Creative Commons license on SourceForge, with the intention that it can be used and extended by the cloud computing research community.

Trading Experiments using Financial Agents in a Simulated Cloud Computing Commodity Market

In September 2012, Amazon, the leading Infrastructure as a Service (IaaS) provider, launched a secondary marketplace venue for users to buy and sell cloud resources between themselvesâ??the Amazon EC2 Reserved Instance Marketplace (ARIM). ARIM is designed to encourage users to purchase more long-term reserved instances, thus generating more stable demand for the provider and additional revenue through commission on sales. In this paper, we model ARIM using a multi-agent simulation model populated with zero-intelligence plus (ZIP) financial trading agents. We demonstrate that ARIM offers a new opportunity for market makers (MMs) to profit from buying and selling resources, but suggest that this opportunity may be fleeting. We also demonstrate that altering the market mechanism from a retail market (where only sellers post offers; similar to ARIM) to a continuous double auction (where both buyers and sellers post offers) can result in higher sale prices and therefore higher commissions. Since IaaS is a multi-billion dollar industry and currently the fastest growing segment of the cloud computing market, we therefore suggest that Amazon may profit from altering the mechanism of ARIM to enable buyers to post bids.

Evidencing the "robot phase transition" in experimental human-algorithmic markets

Johnson, Zhao, Hunsader, Meng, Ravindar, Carran, and Tivnan (2012) recently suggested the existence of a phase transition in the dynamics of financial markets in which there is free interaction between human traders and algorithmic trading systems (‘robots’). Above a particular time-threshold, humans and robots trade with one another; below the threshold all transactions are robot-to-robot. We refer to this abrupt system transition as the ‘robot phase transition’. Here, we conduct controlled experiments where human traders interact with ‘robot’ trading agents in minimal models of electronic financial markets to see if correlates of the two regimes suggested by Johnson et al. (2012) occur in such laboratory conditions. Our results indicate that when trading robots act on a super-human timescale, the market starts to fragment, with statistically lower human-robot interactions than we would expect from a fully mixed market. We tentatively conclude that this is the first empirical evidence for the robot phase transition occurring under controlled experimental conditions.These terms are compatible with the Creative Commons Attribution License 4.0 and the Open Data Commons Attribution License, both of which license copyright and database rights. This means that when the Information is adapted and licensed under either of those licences, you automatically satisfy the conditions of the OGL when you comply with the other licence. The OGLv3.0 is Open Definition compliant.

Behavioural Investigations of Financial Trading Agents Using Exchange Portal (ExPo)

Some major financial markets are currently reporting that 50 % or more of all transactions are now executed by automated trading systems (ATS). To understand the impact of ATS proliferation on the global financial markets, academic studies often use standard reference strategies, such as “AA” and “ZIP”, to model the behaviour of real trading systems. Disturbingly, we show that the reference algorithms presented in the literature are ambiguous, thus reducing the validity of strict comparative studies. As a remedy, we suggest disambiguated standard implementations of AA and ZIP. Using Exchange Portal (ExPo), an open-source financial exchange simulation platform designed for real-time behavioural economic experiments involving human traders and/or trader-agents, we study the effects of disambiguating AA and ZIP, before introducing a novel method of assignment-adaptation (ASAD). Experiments show that introducing ASAD agents into a market with shocks can produce counter-intuitive market dynamics.