Analysis of Emergent Selection Pressure in Evolutionary Algorithm and Machine Learner Offspring Filtering Hybrids

In response to “Digital Darwinism”, people, organisations and society need to adapt to the different characteristics of digital information.[...]

Biotechnological potential of cold-adapted enzymes

Selection pressure restrains the selection of individuals from the current population to produce a new population in the next generation. It gives individuals of higher quality a higher probability of being used to create the next generation so that evolutionary algorithms (EAs) can focus on promising regions in the search space. An evolutionary learning process is dynamic and requires different selection pressures at different learning stages in order to speed up convergence or avoid local optima. Therefore, it is desirable for selection mechanisms to be able to automatically tune selection pressure during evolution. Tournament selection is a popular selection method in EAs, especially genetic algorithms and genetic programming (GP). This paper focuses on tournament selection and shows that the standard tournament selection scheme is unaware of the dynamics in the evolutionary process and that the standard tournament selection scheme is unable to tune selection pressure automatically. This paper then presents a novel approach which integrates the knowledge of the fitness rank distribution (FRD) of a population into tournament selection. Through mathematical modeling, simulations, and experimental study in GP, this paper shows that the new approach is effective and using the knowledge of FRD is a promising way to modify the standard tournament selection method for tuning the selection pressure dynamically and automatically along evolution.

The Pareto dominance‐based evolutionary algorithms can effectively address multiobjective optimization problems (MOPs). However, when dealing with many‐objective optimization problems with more than three objectives (MaOPs), the Pareto dominance relationships cannot effectively distinguish the nondominated solutions in high‐dimensional spaces. With the increase of the number of objectives, the proportion of dominance‐resistant solutions (DRSs) in the population rapidly increases, which leads to insufficient selection pressure. In this paper, to address the challenges on MaOPs, a knee point‐driven many‐objective evolutionary algorithm with adaptive switching mechanism (KPEA) is proposed. In KPEA, the knee points determined by an adaptive strategy are introduced for not only mating selection but also environmental selection, which increases the probability of generating excellent offspring. In addition, to remove dominance‐resistant solutions (DRSs) in the population, an interquartile range method is adopted, which enhances the selection pressure. Moreover, a novel adaptive switching mechanism between angle‐based selection and penalty for selecting solutions is proposed, which is aimed at achieving a balance between convergence and diversity. To validate the performance of KPEA, it is compared with five state‐of‐the‐art many‐objective evolutionary algorithms. All algorithms are evaluated on 20 benchmark problems, i.e., WFG1‐9, MaF1, and MaF4‐13 with 3, 5, 8, and 10 objectives. The experimental results demonstrate that KPEA outperforms the compared algorithms in terms of HV and IGD in most of the test instances.

Oil production from tight oil reservoirs has become economically feasible because of the combination of horizontal drilling and multistage hydraulic fracturing. Optimal fracture design plays a critical role in successful economical production from a tight oil reservoir. However, many complex parameters such as fracture spacing and fracture half-length make fracturing treatments costly and uncertain. To improve fracture design, it is essential to determine reasonable ranges for these parameters and to evaluate their effects on well performance and economic feasibility. In traditional analytical and numerical simulation methods, many simplifications and assumptions are introduced for artificial fracture characterization and gas percolation mechanisms, and their implementation process remains complicated and computationally inefficient. Most previous studies on big data-driven fracturing parameter optimization have been based on only a single output, such as expected ultimate recovery, and few studies have integrated machine learning with evolutionary algorithms to optimize fracturing parameters based on time-series production prediction and economic objectives. This study proposed a novel approach, combining a data-driven model with evolutionary optimization algorithms to optimize fracturing parameters. We established a significant number of static and dynamic data sets representing the geological and developmental characteristics of tight oil reservoirs from numerical simulation. Four production-prediction models based on machine-learning methods—support vector machine, gradient-boosted decision tree, random forest, and multilayer perception—were constructed as mapping functions between static properties and dynamic production. Then, to optimize the fracturing parameters, the best machine-learning-based production predictive model was coupled with four evolutionary algorithms—genetic algorithm, differential evolution algorithm, simulated annealing algorithm, and particle swarm optimization—to investigate the highest net present value (NPV). The results show that among the four production-prediction models established, multilayer perception (MLP) has the best prediction performance. Among the evolutionary algorithms, particle swarm optimization (PSO) not only has the fastest convergence speed but also the highest net present value. The optimal fracturing parameters for the study area were identified. The hybrid MLP-PSO model represents a robust and convenient method to forecast the time-series production and to optimize fracturing parameters by reducing manual tuning.

Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper.

Proteins are cellular macromolecules made up of linear chains of amino acids that adopt a unique three-dimensional structure to carry out specific biological functions. Amongst various computational approaches developed for Protein Structure Prediction (PSP), ab initio methods perform the prediction without prior knowledge of known structures. However, due to huge computational complexity incurred by these methods, simplified hydrophobic-polar (HP) models are often used for the investigations. Despite this simplicity, PSP continues to be a hard combinatorial optimization problem requiring development of efficient heuristic search techniques. Memetic algorithm (MA), with its flexible architecture, encompasses the strength of both local and global search and has shown promise in solving problems with complex search landscape, including PSP. This thesis is focused on developing an effective ab initio technique under a suitable MA framework for solving the PSP problem using low resolution HP models. To enhance the effectiveness of the MA, a mechanism is devised to include knowledge from the problem domain by exploiting the concept of hydrophobic-core (H-core) formation. The size of the maximal possible H-core is estimated and the approximation is validated by our proposed deterministic search technique. Further, a new knowledge-based initial population generation technique ensures generating conformations which are not only valid, but also maintain diversity in the population. This helps to commence the search with good seeds leading towards faster convergence. While solving the PSP problem using MA, the greatest intricacy arises from the complex nature of its multi-modal landscape. Like all the evolutionary algorithms (EAs), MAs have the tendency to get trapped into local minima due to selection pressure and genetic drift induced by the genetic operators, and techniques are necessary to surmount this difficulty. A novel parental selection technique called “Adaptive Strategy for Assortative Mating” is developed that dynamically distributes the re production opportunity among the individuals of the population to boost the effectiveness of cross-over. Further, a new survival selection strategy, namely “Sib-based Survival Selection”, prevents the rapid spread of genetic material from a particular individual to other members in the population, ensuring the concurrent preservation of several potential solutions, and restricting premature convergence. A new diversification mechanism, namely “Memory-based Diversification”, systematically diversifies the population by introducing new genetic material whenever it gets stuck at local peaks and allows it to proceed for further progress. An extensive experimental analysis is also reported on the influence of all three techniques proposed to maintain the diversity of population. Furthermore, in order to carry out the conformational search efficiently, especially with the increase in sequence length, the optimization technique is designed such that it provides an effective balance between exploration and exploitation. A robust Multimodal Memetic Framework has been developed where optimization is accomplished in hierarchical stages. The population of each stage is partitioned into three different states: an Exploratory state to locate promising peaks, an Exploitative state to fine-tune the solutions found in the Exploratory state of the previous stage, and finally, a Central state to carry out the formation of potential solutions to explore the undiscovered regions. Non-identical genetic operators have been implemented in each state to balance the exploration and exploitation by employing necessary selection pressure and diversity. A non-isomorphic encoding based technique has been developed to capture the sub-structures that are representing a particular region. Furthermore, a novel local search technique exploits already explored fit individuals by employing the knowledge of sub-structures. The efficacy of the proposed framework is demonstrated with an adequate number of distinctive data sets and comparison with state-of-the-art methods.

This chapter describes the use of biologically inspired Evolutionary Algorithms (EAs) to create designs for implementation on a reconfigurable logic device. Previous work on Evolvable Hardware (EHW) is discussed with a focus on timing problems for digital circuits. An EA is developed that describes the circuit using a Hardware Description Language (HDL) in a Cartesian Genetic Programming (CGP) framework. The use of an HDL enabled a commercial hardware simulator to be used to evaluate the evolved circuits. Timing models are included in the simulation allowing sequential circuits to be created and assessed. The aim of the work is to develop an EA that is able to create time dependent circuity using the versatility of a HDL and a hardware timing simulator. The variation in the circuit timing from the placement of the logic components, led to an environment with a selection pressure that promoted a more robust design. The results show the creation of both combinatorial and sequential circuits.

Integrating renewable generation into the existing electricity grid to reduce Greenhouse Gas (GHG) emissions involves several challenges. These include, e.g., volatile generation and demand, and can be overcome by increasing flexibility in the grid. One possibility to provide this flexibility is the optimized scheduling of Distributed Energy Resources (DERs). Such a scheduling task requires a powerful optimization algorithm, such as Evolutionary Algorithms (EAs). However, EAs can produce poor solution quality w.r.t. solution time when solving complex and large scale scheduling tasks of DERs. Hence, in our work, a concept for improving the EA optimization process for scheduling DERs is presented and evaluated. In this concept, Machine Learning (ML) algorithms learn from already found solutions to predict the optimization quality in advance. By this, the computational effort of the EA is directed to particularly difficult areas of the search space. This is achieved by dynamic interpretation and consequent interval length assignment of the solutions proposed by the EA. We evaluate our approach by comparing two experiments and show that our novel concept leads to a significant increase of the evaluated fitness by up to 9.4%.

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS.

The GRF gene family plays an important role in plant growth and development as regulators involved in plant hormone signaling and metabolism. However, the Juglandaceae GRF gene family remains to be studied. Here, we identified 15, 15, 19, and 20 GRF genes in J. regia, C. illinoinensis, J. sigillata, and J. mandshurica, respectively. The phylogeny shows that the Juglandaceae family GRF is divided into two subfamilies, the ε-group and the non-ε-group, and that selection pressure analysis did not detect amino acid loci subject to positive selection pressure. In addition, we found that the duplications of the Juglandaceae family GRF genes were all segmental duplication events, and a total of 79 orthologous gene pairs and one paralogous homologous gene pair were identified in four Juglandaceae families. The Ka/KS ratios between these homologous gene pairs were further analyzed, and the Ka/KS values were all less than 1, indicating that purifying selection plays an important role in the evolution of the Juglandaceae family GRF genes. The codon bias of genes in the GRF family of Juglandaceae species is weak, and is affected by both natural selection pressure and base mutation, and translation selection plays a dominant role in the mutation pressure in codon usage. Finally, expression analysis showed that GRF genes play important roles in pecan embryo development and walnut male and female flower bud development, but with different expression patterns. In conclusion, this study will serve as a rich genetic resource for exploring the molecular mechanisms of flower bud differentiation and embryo development in Juglandaceae. In addition, this is the first study to report the GRF gene family in the Juglandaceae family; therefore, our study will provide guidance for future comparative and functional genomic studies of the GRF gene family in the Juglandaceae specie.

In wireless communication systems, high-performance antenna, microwave, and radio frequency design systems are essential to meet end-user requirements. As demand for these components increases, it's crucial to design optimized structures in a short amount of time with guaranteed best results. This has led to the need for a higher level of intelligence in the design process. Artificial intelligence (AI) techniques such as evolutionary algorithms (EAs), machine learning (ML), deep learning (DL), and knowledge representation have been widely used to find parameter values of antenna and microwave components, leading to optimized designs in minimum processing time and overcoming long processing times and poor results. This chapter focuses on the major AI methods in the area of antenna, microwave, and other radio frequency (RF) components, including phase shifters, intelligent reflective surfaces (RIS), waveguides, filters, stubs, etc. The chapter discusses different EAs and ML algorithms and their use in optimizing antenna and microwave designs.

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

Evolutionary Algorithms have been widely used to solve difficult constrained optimization problems. However, Evolutionary algorithms are intrinsically unconstrained optimization techniques. Constraint handling is mostly incorporated additionally and its choice has great bearing on the quality of the solution. Stochastic Ranking was introduced as an improvement over Feasibility Rules for handling constraints in Evolutionary Optimization. It is widely believed that stochastic ranking is currently the best-known technique for handling constraints. However, a fair comparative study has never been attempted in the literature, where by the performance of both the constraint handling technique is compared on the same Evolutionary Algorithm. This paper fairly compares the performance of both the constraint handling techniques on the same Evolutionary Algorithm over a set of parameters like feasibility rate, successful run, success rate and success performance in addition to objective function value and number of function evaluations. The results put a question mark over the belief that Feasibility Rules are worse than Stochastic Ranking.

In the past several years, it has become apparent that the effectiveness of Pareto-dominance-based multiobjective evolutionary algorithms deteriorates progressively as the number of objectives in the problem, given by M , grows. This is mainly due to the poor discriminability of Pareto optimality in many-objective spaces (typically M ≥ 4 ). As a consequence, research efforts have been driven in the general direction of developing solution ranking methods that do not rely on Pareto dominance (e.g., decomposition-based techniques), which can provide sufficient selection pressure. However, it is still a nontrivial issue for many existing non-Pareto-dominance-based evolutionary algorithms to deal with unknown irregular Pareto front shapes. In this article, a new many-objective evolutionary algorithm based on the generalization of Pareto optimality (GPO) is proposed, which is simple, yet effective, in addressing many-objective optimization problems. The proposed algorithm used an "( M-1 ) + 1" framework of GPO dominance, ( M-1 )-GPD for short, to rank solutions in the environmental selection step, in order to promote convergence and diversity simultaneously. To be specific, we apply M symmetrical cases of ( M-1 )-GPD, where each enhances the selection pressure of M-1 objectives by expanding the dominance area of solutions, while remaining unchanged for the one objective left out of that process. Experiments demonstrate that the proposed algorithm is very competitive with the state-of-the-art methods to which it is compared, on a variety of scalable benchmark problems. Moreover, experiments on three real-world problems have verified that the proposed algorithm can outperform the others on each of these problems.