waashk/dblp huggingface dataset:
evolutionary market agents for resource allocation in decentralised systems. we introduce self-interested evolutionary market agents, which act on behalf of service providers in a large decentralised system, to adaptively price their resources over time. our agents competitively co-evolve in the live market, driving it towards the bertrand equilibrium, the non-cooperative nash equilibrium, at which all sellers charge their reserve price and share the market equally. we demonstrate that this outcome results in even load-balancing between the service providers.our contribution in this paper is twofold; the use of on-line competitive co-evolution of self-interested service providers to drive a decentralised market towards equilibrium, and a demonstration that load-balancing behaviour emerges under the assumptions we describe.unlike previous studies on this topic, all our agents are entirely self-interested; no cooperation is assumed. this makes our problem a non-trivial and more realistic one.
actuation constraints and artificial physics control. swarm systems for multiagent control rely on natural models of behavior. such models both predict simulated natural behavior and provide control instructions to the underlying agents. these two roles can differ when, for example, controlling nonholonomic robots incapable of executing some control suggestions from the system. we consider a simple physicomimetics system and examine the effects of actuation constraint on that system in terms of its ability to stabilize in regular formations, as well as the impact of such constraints on learning control parameters. we find that in the cases we considered, physicomimetics is surprisingly robust to certain types of actuation constraint.
supervised and evolutionary learning of echo state networks. a possible alternative to topology fine-tuning for neural network (nn) optimization is to use echo state networks (esns), recurrent nns built upon a large reservoir of sparsely randomly connected neurons. the promises of esns have been fulfilled for supervised learning tasks, but unsupervised ones, e.g. control problems, require more flexible optimization methods --- such as evolutionary algorithms. this paper proposes to apply cma-es, the state-of-the-art method in evolutionary continuous parameter optimization, to the evolutionary learning of esn parameters. first, a standard supervised learning problem is used to validate the approach and compare it to the standard one. but the flexibility of evolutionary optimization allows us to optimize not only the outgoing weights but also, or alternatively, other esn parameters, sometimes leading to improved results. the classical double pole balancing control problem is then used to demonstrate the feasibility of evolutionary (i.e. reinforcement) learning of esns. we show that the evolutionary esn obtain results that are comparable with those of the best topology-learning methods.
qfcs: a fuzzy lcs in continuous multi-step environments with continuous vector actions. this paper introduces the qfcs, a new approach to fuzzy learning classifier systems. qfcs can solve the multistep reinforcement learning problem in continuous environments and with a set of continuous vector actions. rules in the qfcs are small fuzzy systems. qfcs uses a q-learning algorithm to learn the mapping between inputs and outputs. this paper presents results that show that qfcs can evolve rules to represent only those parts of the input and action space where the expected values are important for making decisions. results for the qfcs are compared with those obtained by q-learning with a high discretization to show that the new approach converges in a way similar to how q-learning does for one-dimension problems with an optimal solution, and for two dimensions qfcs learns suboptimal solutions while it is difficult for q-learning to converge due to that high discretization.
extreme value based adaptive operator selection. credit assignment is an important ingredient of several proposals that have been made for adaptive operator selection. instead of the average fitness improvement of newborn offspring, this paper proposes to use some empirical order statistics of those improvements, arguing that rare but highly beneficial jumps matter as much or more than frequent but small improvements. an extreme value based credit assignment is thus proposed, rewarding each operator with the best fitness improvement observed in a sliding window for this operator. this mechanism, combined with existing adaptive operator selection rules, is investigated in an ec-like setting. first results show that the proposed method allows both the adaptive pursuit and the dynamic multi-armed bandit selection rules to actually track the best operators along evolution.
comparison of adaptive approaches for differential evolution. the evaluation of optimization algorithms and especially the analysis of adaptive variants is often complicated because several features are modified concurrently. for differential evolution these features may be adaptation of parameters, adjustment of the strategy and addition of local search or other special operators. thus, it is difficult to analyze which of these procedures is actually responsible for changes in the performance. therefore, in this work several adaptive algorithms are studied in-depth by monitoring performance changes for individual components of these algorithms to examine their effectiveness. the results show among others that the performance can be significantly improved by employing strategy control.
evolution strategies for direct policy search. the covariance matrix adaptation evolution strategy (cma-es) is suggested for solving problems described by markov decision processes. the algorithm is compared with a state-of-the-art policy gradient method and stochastic search on the double cart-pole balancing task using linear policies. the cma-es proves to be much more robust than the gradient-based approach in this scenario.
sub-tree swapping crossover, allele diffusion and gp convergence. we provide strong evidence that sub-tree swapping crossover when applied to tree-based representations will cause alleles (node labels) to diffuse within length classes. for a-ary trees we provide further confirmation that all programs are equally likely to be sampled within any length class when sub-tree swapping crossover is applied in the absence of selection and mutation. therefore, we propose that this form of search is unbiased - within length classes - for a-ary trees. unexpectedly, however, for mixed-arity trees this is not found and a more complicated form of search is taking place where certain tree shapes, hence programs, are more likely to be sampled than others within each class. we examine the reasons for such shape bias in mixed arity representations and provide the practitioner with a thorough examination of sub-tree swapping crossover bias. the results of this, when combined with crossover length bias research, explain genetic programming's lack of structural convergence during later stages of an experimental run. several operators are discussed where a broader form of convergence may be detected in a similar way to that found in genetic algorithm experimentation.
improved lower limits for pheromone trails in ant colony optimization. ant colony optimization algorithms were inspired by the foraging behavior of ants that accumulate pheromone trails on the shortest paths to food. some aco algorithms employ pheromone trail limits to improve exploration and avoid stagnation by ensuring a non-zero probability of selection for all trails. the max-min ant system (mmas) sets explicit pheromone trail limits while the ant colony system (acs) has implicit pheromone trail limits. stagnation still occurs in both algorithms with the recommended pheromone trail limits as the relative importance of the pheromone trails increases (¿> 1). improved estimates of the lower pheromone trail limit (¿ min ) for both algorithms help avoid stagnation and improve performance for ¿> 1. the improved estimates suggest a general rule to avoid stagnation for stochastic algorithms with explicit or implicit limits on exponential values used in proportional selection.
large-scale optimization of non-separable building-block problems. this paper presents principled results demonstrating how the identification and exploitation of variable dependencies by means of artificial neural network powered online model building, combined with a model based local-search, opens the way towards large-scale optimization of hard, non-separable building-block problems.
optimization of feature processing chain in music classification by evolution strategies. in this paper a new method based on evolution strategies (es) is presented to optimize a classifier for personal music categories. the user assigns songs to multiple personal music categories: examples from each category are selected in order to train a category-specific classifier using musical features as input. the classifier then ranks all songs according to their similarity to the category examples. since an exhaustive search for parameters maximizing the classifier performance is not feasible an es is applied. the experiments show a significant performance increase for various music categories due to the es optimization.
a memetic algorithm for the delineation of local labour markets. given a territory composed of basic geographical units, the delineation of local labour market areas (llmas) can be seen as a problem in which those units are grouped subject to multiple constraints. in previous research, standard genetic algorithms were not able to find valid solutions, and a specific evolutionary algorithm was developed. the inclusion of multiple ad hoc operators allowed the algorithm to find better solutions than those of a widely-used greedy method. however, the percentage of invalid solutions was still very high. in this paper we improve that evolutionary algorithm through the inclusion of (i) a reparation process, that allows every invalid individual to fulfil the constraints and contribute to the evolution, and (ii) a hillclimbing optimisation procedure for each generated individual by means of an appropriate reassignment of some of its constituent units. we compare the results of both techniques against the previous results and a greedy method.
nature-inspired synthesis of rational protocols. rational cryptography is an emerging field which combines aspects traditionally related to security with concepts described in economic theoretical frameworks. for example, it applies game theory concepts to address security problems arising when executing cryptographic protocols. the aim is to replace the assumption of a worst---case attacker by the notion of rational agents that try to maximize their payoffs. in this work, we define a formal framework and a meta---heuristic technique for the automated synthesis of multi---party rational exchange security (m---res) protocols. we provide experimental results for a simple scenario where a 3---party rational exchange protocol is automatically designed.
a set-based particle swarm optimization method. the representation used in particle swarm optimization (pso) is an n-dimensional vector. if you want to apply the pso method, you have to encode your problem as fix-sized vector. but many problem domains have solutions of unknown sizes as for instance in data clustering where you often don't know the number of clusters in advance.in this paper a set-based pso is proposed which replaces the position and velocity vectors by position and velocity sets realizing this way a pso with variable length representation. all operations of the pso update equations are redefined in an appropriate manner. additionally, an operator reducing set bloating effects is introduced.the presented approach is applied to well-known data clustering problems and performs better as other algorithms on them.
runtime analyses for using fairness in evolutionary multi-objective optimization. it is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the pareto front. in this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness introduced by laumanns et al.[7]. this mechanism tries to balance the number of offspring of all individuals in the current population. we rigorously analyze the runtime behavior of different fairness mechanisms and present showcase examples to point out situations where the right mechanism can speed up the optimization process significantly.
evolving xslt stylesheets for document transformation. this paper presents a new version of an evolutionary algorithm that creates xslt programs from its intended input and output. xslt is a general purpose, document-oriented functional language, generally used to transform xml documents (or, in general, solve any problem that can be coded as an xml document). previously, a solution that solved the problem efficiently was proposed. in this paper, we improve on those results by testing different fitness functions, adding a new operator and changing the type of desired output document that can be obtained. the experiments show that the best results are obtained without considering the xslt length and including this new operator.
evaluation and diversity in co-evolution. this paper studies the performance of four alternative evaluation methods; two instances of the exponential moving average, the elo-rating and the glicko-rating method. these methods are tested in a co-evolutionary setup using the lint-game, which is known to be problematic under co-evolutionary conditions. besides the different evaluation approaches, two methods aimed at preserving diversity are tested. by using the objective fitness correlation as an analytical tool for monitoring accuracy of evaluation, it is shown that actual performance of an evaluation method strongly depends on whether co-evolutionary failure occurs and that a multi-modal approach to the lint-problem is effective in maintaining stable progress over time.
a study of convergence speed in multi-objective metaheuristics. an open issue in multi-objective optimization is designing metaheuristics that reach the pareto front using a low number of function evaluations. in this paper, we adopt a benchmark composed of three well-known problem families (zdt, dtlz, and wfg) and analyze the behavior of six state-of-the-art multi-objective metaheuristics, namely, nsga-ii, spea2, paes, omopso, abyss, and mocell, according to their convergence speed, i.e., the number of evaluations required to obtain an accurate pareto front. by using the hypervolume as a quality indicator, we measure the algorithms converging faster, as well as their hit rate over 100 independent runs. our study reveals that modern multi-objective metaheuristics such as mocell, omopso, and abyss provide the best overall performance, while nsga-ii and mocell achieve the best hit rates.
lower bounds for evolution strategies using vc-dimension. we derive lower bounds for comparison-based or selection-based algorithms, improving existing results in the continuous setting, and extending them to non-trivial results in the discrete case. this is achieved by considering the vc-dimension of the level sets of the fitness functions; results are then obtained through the use of sauer's lemma. in the special case of optimization of the sphere function, improved lower bounds are obtained by bounding the possible number of sign conditions realized by some systems of equations.
spam: set preference algorithm for multiobjective optimization. this paper pursues the idea of a general multiobjective optimizer that can be flexibly adapted to arbitrary user preferences--assuming that the goal is to approximate the pareto-optimal set. it proposes the set preference algorithm for multiobjective optimization (spam) the working principle of which is based on two observations: (i) current multiobjective evolutionary algorithms (moeas) can be regarded as hill climbers on set problems and (ii) specific user preferences are often (implicitly) expressed in terms of a binary relation on pareto set approximations. spam realizes a (1 + 1)-strategy on the space of pareto set approximations and can be used with any type of set preference relations, i.e., binary relations that define a total preorder on pareto set approximations. the experimental results demonstrate for a range of set preference relations that spam provides full flexibility with respect to user preferences and is effective in optimizing according to the specified preferences. it thereby offers a new perspective on preference-guided multiobjective search.
on the run-time dynamics of a peer-to-peer evolutionary algorithm. in this paper we propose an improvement on a fully distributed peer-to-peer (p2p) evolutionary algorithm (ea) based on autonomous selection. autonomous selection means that individuals decide on their own state of reproduction and survival without any central control, using instead estimations about the global population state for decision making. the population size varies at run-time as a consequence of such a decentralized reproduction and death of individuals. in order to keep it stable, we propose a self-adjusting mechanism which has been shown successful in three different search landscapes. key are the estimations about fitness and size of the population as provided by a gossiping algorithm. such an algorithm requires several rounds to collect the information while the individuals have to wait for synchronization. as an improvement, we propose a completely asynchronous ea which does not need waiting times. the results show that our approach outperforms quantitatively the execution time of the synchronous version.
particle filter with swarm move for optimization. we propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (pso). in this way, the pso update equation is treated as the system dynamic in the state space model, while the objective function in optimization problem is designed as the observation/measurement in the state space model. particle filter method is then applied to track the dynamic movement of the particle swarm and therefore results in a novel stochastic optimization tool, where the ability of pso in searching the optimal position can be embedded into the particle filter optimization method. finally, simulation results show that the proposed novel approach has significant improvement in both convergence speed and final fitness in comparison with the pso algorithm over a set of standard benchmark problems.
a local search based evolutionary multi-objective optimization approach for fast and accurate convergence. a local search method is often introduced in an evolutionary optimization technique to enhance its speed and accuracy of convergence to true optimal solutions. in multi-objective optimization problems, the implementation of a local search is a non-trivial task, as determining a goal for the local search in presence of multiple conflicting objectives becomes a difficult proposition. in this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and include it as a search operator of an emo algorithm. simulation results with nsga-ii on a number of two to four-objective problems with and without the local search approach clearly show the importance of local search in aiding a computationally faster and more accurate convergence to pareto-optimal solutions. the concept is now ready to be coupled with a faster and more accurate diversity-preserving procedure to make the overall procedure a competitive algorithm for multi-objective optimization.
formally testing liveness by means of compression rates. we present a formal method to determine whether there exist living creatures in a given computational environment. our proposal is based on studying the evolution of the entropy of the studied system. in particular, we check whether there exist entities decreasing the entropy in some parts, while increasing it in the rest of the world, which fits into the well-known maximum entropy production principle. the entropy of a computational environment is measured in terms of its compression rate with respect to some compression strategy. some life-related notions such as biodiversity are quantified as well. these ideas are presented by means of formal definitions. a toy example where a simple living structure is identified in a video stream is presented, and some results are reported.
how single ant aco systems optimize pseudo-boolean functions. we undertake a rigorous experimental analysis of the optimization behavior of the two most studied single ant aco systems on several pseudo-boolean functions. by tracking the behavior of the underlying random processes rather than just regarding the resulting optimization time, we gain additional insight into these systems. a main finding is that in those cases where the single ant aco system performs well, it basically simulates the much simpler (1+1) evolutionary algorithm.
enhancing the performance of maximum-likelihood gaussian edas using anticipated mean shift. many estimation---of---distribution algorithms use maximum-likelihood (ml) estimates. for discrete variables this has met with great success. for continuous variables the use of ml estimates for the normal distribution does not directly lead to successful optimization in most landscapes. it was previously found that an important reason for this is the premature shrinking of the variance at an exponential rate. remedies were subsequently successfully formulated (i.e. adaptive variance scaling (avs) and standard---deviation ratio triggering (sdr)). here we focus on a second source of inefficiency that is not removed by existing remedies. we then provide a simple, but effective technique called anticipated mean shift (ams) that removes this inefficiency.
theoretical analysis of initial particle swarm behavior. in this paper, particle trajectories of pso algorithms in the first iteration are studied. we will prove that many particles leave the search space at the beginning of the optimization process when solving problems with boundary constraints in high-dimensional search spaces. three different velocity initialization strategies will be investigated, but even initializing velocities to zero cannot prevent this particle swarm explosion. the theoretical analysis gives valuable insight into pso in high-dimensional bounded spaces, and highlights the importance of bound handling for pso: as many particles leave the search space in the beginning, bound handling strongly influences particle swarm behavior. experimental investigations confirm the theoretical results.
diversity maintenance mechanism for multi-objective genetic algorithms using clustering and network inversion. one of the major issues in applying multi-objective genetic algorithms to real-world problems is how to reduce the large number of evaluations. the simplest approach is a search with a small population size. however, the diversity of solutions is often lost with such a search. to overcome this difficulty, this paper proposes a diversity maintenance mechanism using clustering and network inversion that is capable of preserving diversity by relocating solutions. in addition, the proposed mechanism adopts clustering of training data sets to improve the accuracy of relocation. the results of numerical experiments on test functions and diesel engine emission and fuel economy problems showed that the proposed mechanism provided solutions with high diversity even when the search was performed with a small number of solutions.