Günter Bachelier asked if I might publicize this to the Genetic Programming and Evolvable Machines community, and I do think that there may be interest.
The goal of this competition is to produce, presumably by evolution, 2D tiling patterns with no gaps or overlaps, with the initial targets being the known tilings that are available in a public database. The competition apparently originated in work in evolutionary art, but it may be relevant to other image processing applications as well.
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
In this paper we have developed an algorithm for many-objective optimization problems, which will work more quickly than existing ones, while offering competitive performance. The algorithm periodically reorders the objectives based on their conflict status and selects a subset of conflicting objectives for further processing. We have taken differential evolution multiobjective optimization (DEMO) as the underlying metaheuristic evolutionary algorithm, and implemented the technique of selecting a subset of conflicting objectives using a correlation-based ordering of objectives. The resultant method is called $alpha $ -DEMO, where $alpha $ is a parameter determining the number of conflicting objectives to be selected. We have also proposed a new form of elitism so as to restrict the number of higher ranked solutions that are selected in the next population. The $alpha $ -DEMO with the revised elitism is referred to as $alpha $ -DEMO-revised. Extensive results of the five DTLZ functions show that the number of objective computations required in the proposed algorithm is much less compared to the existing algorithms, while the convergence measures are competitive or often better. Statistical significance testing is also performed. A real-life application on structural optimization of factory shed truss is demonstrated.
In this paper, we investigate how behavioral contagion in terms of mimetic strategy learning within a social network would affect the asset price dynamics. The characteristics of this paper are as follows. First, traders are characterized by bounded rationality and their adaptive learning behavior is represented by the genetic programming algorithm. The use of the genetic programming algorithm allows traders to freely form forecasting strategies with a great potential of variety in functional forms, which are not predetermined but may be fundamental or technical or any mix of these two broad categories, as they need to adapt to the time-varying market environment. The evolutionary nature of the genetic programming algorithm has its merit for modeling mimetic behavior in the context of information transmission in that, other than making duplicates of an entire trading rule as if a mind-reading technique exists, strategy imitation could take place down to the level of building blocks that genetic operators work out or pieces of information that constitute a strategy and are more ready to be transmitted via word-of-mouth communication, which is more intuitive compared to the existing literature. Second, the traders are spatially heterogeneous based on their positions in social networks. Mimetic learning thus takes part in local interactions among traders that are directly tied with each other when they evolve their trading strategies according to the relative performance of their own and their neighbors’. Therefore, specifically, we aim to analyze the effect of network topologies, i.e., a regular lattice, a small-world, a random network, a fully connected network, and a preferential attachment network, on market dynamics regarding price distortion, volatility, and trading volume, as information diffuses across these different social network structures.
In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs.
Decomposition-based evolutionary algorithms have been quite successful in solving optimization problems involving two and three objectives. Recently, there have been some attempts to exploit the strengths of decomposition-based approaches to deal with many objective optimization problems. Performance of such approaches are largely dependent on three key factors: 1) means of reference point generation; 2) schemes to simultaneously deal with convergence and diversity; and 3) methods to associate solutions to reference directions. In this paper, we introduce a decomposition-based evolutionary algorithm wherein uniformly distributed reference points are generated via systematic sampling, balance between convergence and diversity is maintained using two independent distance measures, and a simple preemptive distance comparison scheme is used for association. In order to deal with constraints, an adaptive epsilon formulation is used. The performance of the algorithm is evaluated using standard benchmark problems, i.e., DTLZ1-DTLZ4 for 3, 5, 8, 10, and 15 objectives, WFG1-WFG9, the car side impact problem, the water resource management problem, and the constrained ten-objective general aviation aircraft design problem. Results of problems involving redundant objectives and disconnected Pareto fronts are also included in this paper to illustrate the capability of the algorithm. The study clearly highlights that the proposed algorithm is better or at par with recent reference direction-based approaches for many objective optimization.
Understanding how search operators interact with solution representation is a critical step to improving search. In Cartesian genetic programming (CGP), and genetic programming (GP) in general, the complex genotype to phenotype map makes achieving this understanding a challenge. By examining aspects such as tuned parameter values, the search quality of CGP variants at different problem difficulties, node behavior, and offspring replacement properties we seek to better understand the characteristics of CGP search. Our focus is two-fold: creating methods to prevent wasted CGP evaluations (skip, accumulate, and single) and creating methods to overcome CGPs search limitations imposed by genome ordering (reorder and DAG). Our results on Boolean problems show that CGP evolves genomes that are highly inactive, very redundant, and full of seemingly useless constants. On some tested problems we found that less than 1% of the genome was actually required to encode the evolved solution. Furthermore, traditional CGP ordering results in large portions of the genome that are never used by any ancestor of the evolved solution. Reorder and DAG allow evolution to utilize the entire genome. More generally, our results suggest that skip-reorder and single-reorder are most likely to solve hard problems using the least number of evaluations and the least amount of time while better avoiding degenerate behavior.
Nonlinear equation systems may have multiple optimal solutions. The main task of solving nonlinear equation systems is to simultaneously locate these optimal solutions in a single run. When solving nonlinear equation systems by evolutionary algorithms, usually a nonlinear equation system should be transformed into a kind of optimization problem. At present, various transformation techniques have been proposed. This paper presents a simple and generic transformation technique based on multiobjective optimization for nonlinear equation systems. Unlike the previous work, our transformation technique transforms a nonlinear equation system into a biobjective optimization problem that can be decomposed into two parts. The advantages of our transformation technique are twofold: 1) all the optimal solutions of a nonlinear equation system are the Pareto optimal solutions of the transformed problem, which are mapped into diverse points in the objective space, and 2) multiobjective evolutionary algorithms can be directly applied to handle the transformed problem. In order to verify the effectiveness of our transformation technique, it has been integrated with nondominated sorting genetic algorithm II to solve nonlinear equation systems. The experimental results have demonstrated that, overall, our transformation technique outperforms another state-of-the-art multiobjective optimization based transformation technique and four single-objective optimization based approaches on a set of test instances. The influence of the types of Pareto front on the performance of our transformation technique has been investigated empirically. Moreover, the limitation of our transformation technique has also been identified and discussed in this paper.