Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Results of Evolution Supervised by Genetic Algorithms

A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.Comment: 6 pages, 1 Table, 2 figure

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish

Coevolutionary algorithms for the optimization of strategies for red teaming applications

Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as intelligent agents in a multi-agent system and the idea is to run many computer simulations, pitting the plan of the red team against the plan of blue team. This research project investigated techniques that can support automated red teaming by conducting a systematic study involving a genetic algorithm (GA), a basic coevolutionary algorithm and three variants of the coevolutionary algorithm. An initial pilot study involving the GA showed some limitations, as GAs only support the optimization of a single population at a time against a fixed strategy. However, in red teaming it is not sufficient to consider just one, or even a few, opponent‟s strategies as, in reality, each team needs to adjust their strategy to account for different strategies that competing teams may utilize at different points. Coevolutionary algorithms (CEAs) were identified as suitable algorithms which were capable of optimizing two teams simultaneously for red teaming. The subsequent investigation of CEAs examined their performance in addressing the characteristics of red teaming problems, such as intransitivity relationships and multimodality, before employing them to optimize two red teaming scenarios. A number of measures were used to evaluate the performance of CEAs and in terms of multimodality, this study introduced a novel n-peak problem and a new performance measure based on the Circular Earth Movers‟ Distance. Results from the investigations involving an intransitive number problem, multimodal problem and two red teaming scenarios showed that in terms of the performance measures used, there is not a single algorithm that consistently outperforms the others across the four test problems. Applications of CEAs on the red teaming scenarios showed that all four variants produced interesting evolved strategies at the end of the optimization process, as well as providing evidence of the potential of CEAs in their future application in red teaming. The developed techniques can potentially be used for red teaming in military operations or analysis for protection of critical infrastructure. The benefits include the modelling of more realistic interactions between the teams, the ability to anticipate and to counteract potentially new types of attacks as well as providing a cost effective solution

Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer optimization framework, on non-separable functions. First, we reveal empirical reasons of why decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores

An Effective Ensemble Approach for Spam Classification

The annoyance of spam increasingly plagues both individuals and organizations. Spam classification is an important issue to distinguish the spam with the legitimate email or address. This paper presents a neural network ensemble approach based on a specially designed cooperative coevolution paradigm. Each component network corresponds to a separate subpopulation and all subpopulations are evolved simultaneously. The ensemble performance and the Q-statistic diversity measure are adopted as the objectives, and the component networks are evaluated by using the multi-objective Pareto optimality measure. Experimental results illustrate that the proposed algorithm outperforms the traditional ensemble methods on the spam classification problems

On Design Mining: Coevolution and Surrogate Models

© 2017 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines