Artificial Problems Research Articles

Solving artificial ant problem using two artificial bee colony programming versions

Artificialant problem is considered as a sub-problem of robotic path planning. In this study, it is solved using two different methods: artificial bee colony programming and a new version of it called shrinking artificial bee colony programming. The former is a novel evolutionary computation based automatic programming method based on artificial bee colony algorithm and it was previously applied to this problem by the researchers. However, in this study, more comprehensive analyses and comparison study are provided. The shrinking artificial bee colony programming was developed in this study and its basic idea is to reduce the number of food sources, periodically, instead of a constant number used in the artificial bee colony programming. First, some parameter tuning studies were carried out for the shrinking artificial bee colony programming. Then, performances of the artificial bee colony programming, shrinking artificial bee colony programming and some other evolutionary computation based automatic programming methods were compared on Santa Fe and Los Altos Hills trails. Simulation results and the comparison study show that both of the algorithms can be used to solve the artificial ant problem effectively. Furthermore, the periodically decreasing population size property added to the artificial bee colony programming improves the performance of the algorithm on the artificial ant problem. While the proposed approach shows one of the superior performances among the considered methods, the results of the artificial bee colony programming are competitive to the methods in the comparison study.

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, among the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of “genomic parasites”, such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the artificial ant problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA – artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant’s code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.

Solving Five Instances of the Artificial Ant Problem with Ant Colony Optimization

The Artificial Ant problem is a common benchmark problem often used for metaheuristic algorithm performance evaluation. The problem is to find a strategy controlling an agent (called an Artificial Ant) in a game performed on a square toroidal field. Some cells of the field contain “food” pellets, which are distributed along a certain trail. In this paper we use Finite-State Machines (FSM) for strategy representation and present a new algorithm –MuACOsm – for learning finite-state machines. The new algorithm is based on an Ant Colony Optimization algorithm (ACO) and a graph representation of the search space. We compare the new algorithm with a genetic algorithm (GA), evolutionary strategies (ES), a genetic programming related approach and reinforcement learning on five instances of the Artificial Ant Problem.

Diversity Maintenance in Genetic Programming

This paper is motivated by an experimental result that better performing genetic programming runs tend to have higher phenotypic diversity. To maintain phenotypic diversity, we apply implicit fitness sharing and its variant, called unfitness multiplying. To apply these methods to problems in which individuals have infinite kinds of possible behaviours, we classify posible behaviours into 50 achievement levels, and assign a reward or a penalty to each level. In implicit fitness sharing a reward is shared out among individuals with the same achievement level, and in unfitness multiplying a penalty is multiplied by the number of individuals with the same level and is distributed to related individuals. Five benchmark problems (11-multiplexer, sextic polynomial, four-sine, intertwined spiral, and artificial ant problems) are used to illustrate the effect of the methods. The results show that our methods clearly promote diversity and lead population to a smooth frequency distribution of achievement levels, and that our methods usually perform better than the original implicit fitness sharing on success rate and the best (raw) fitness. We also observe that the unfitness multiplying makes a quite different ranking over individuals than the one by the implicit fitness sharing.

Why Do We Care So Much About Mere Games? (And Is This Ethically Defensible?)

Human beings devote extraordinary amounts of time, energy, and attention to activities that, on their own, do not produce anything substantial. Yet we live in a world full of poverty, political injustice, AIDS, starvation, war, and any number of other pressing issues. This raises interesting ethical questions about the human tendency to care so much about mere games, rather like fi ddling with soccer while the world burns. This tendency to fi nd and play games, however, has roots deep in our evolutionary history. I claim that we grew up as a species with games as a constant companion. While the serious moral demands of life require that we keep games in some kind of reasonable perspective, work imperatives do not provide all that is needed for the good life. Our ancestors gravitated to the artifi cial problems provided by games, and so do we. Our passion for games is both very human and entirely understandable.

CO-EVOLUTION OF FINITE STATE MACHINES FOR OPTIMIZATION: PROMOTION OF DEVICES WHICH SEARCH GLOBALLY

In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.

A Robust Evolutionary Algorithm for Training Neural Networks

A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.

A genetic algorithm with adaptive mutations and family competition for training neural networks.

In this paper, we present a new evolutionary technique to train three general neural networks. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to collaborate with each other. Different mutations act as global and local strategies respectively to balance the trade-off between solution quality and convergence speed. Our algorithm is then applied to three different task domains: Boolean functions, regular language recognition, and artificial ant problems. Experimental results indicate that the proposed algorithm is very competitive with comparable evolutionary algorithms. We also discuss the search power of our proposed approach.

Progressive evolution model using a hardware evolution system

A progressive evolution model is proposed in which evolution takes place stepwise to match environmental changes. This model was designed to accelerate evolution. Environmental complexity is defined, and the problem environment progresses in environmental complexity gradually from easy to difficult. A verification system for the model is constructed on a hardware evolution system called AdAM (adaptive architecture methodology) in which each individual circuit takes parallel input sequences and operates on this input. A measure that is suitable for such parallel simultaneous operations schemes is designed to express environmental complexity. Simulations using an artificial ant problem (a modified John Muir trail) show that in the progressive evolution model, circuits can easily evolve complex behaviors.