An analysis of the behavior of simplified evolutionary algorithms on trap functions

Evolutionary algorithms (EAs) have been used to achieve optimal feedforward control in a number of fed-batch fermentation processes. Typically, the optimization purpose is to set the optimal feeding trajectory, being the feeding profile over time given by a piecewise linear function, in order to reduce the number of parameters to the optimization algorithm. In this work, a novel representation scheme for the encoding of the feeding trajectory over time is proposed. Each gene in the variable sized chromosome has two components: a time label and the real value of the variable. The new approach is compared with a traditional real-valued EA, with chromosomes of constant size and fixed discretization steps. Three distinct case studies are presented, taken from previous work from the authors and literature, all considering the optimization of fed-batch fermentation processes. The experimental results show that the proposed approach is capable of results better or at the same level of quality of the best traditional EAs and is able to automatically evolve the best discretization steps for each case, thus simplifying the EA's setup

Evolutionary Negative Selection Algorithms (ENSAs) could be regarded as hybrid algorithms of Evolutionary Algorithms (EAs) and Negative Selection Algorithms (NSAs). The average time complexity of ENSAs on combinational optimization problems has never been studied before. In this paper, the average time complexity of ENSAs on one combinational optimization problem is analyzed. The theoretical results demonstrate that, for the Two Max function, the ENSA with an appropriate matching threshold could perform better than the traditional (N+N) EA. Some simulation experiments on the combinational problem are also done, and the experimental results are consistent with theoretical results.

Jump functions are the most studied non-unimodal benchmark in the theory of evolutionary algorithms (EAs). They have significantly improved our understanding of how EAs escape from local optima. However, their particular structure - to leave the local optimum the EA can only jump directly to the global optimum - raises the question of how representative the recent findings are. For this reason, we propose an extended class Jumpk,δ of jump functions that incorporate a valley of low fitness of width δ starting at distance k from the global optimum. We prove that several previous results extend to this more general class: for all k = o (n1/3) and δ ≤ k, the optimal mutation rate for the (1 + 1) EA is [EQUATION], and the fast (1 + 1) EA runs faster than the classical (1 + 1) EA by a factor super-exponential in δ. However, we also observe that some known results do not generalize: the randomized local search algorithm with stagnation detection, which is faster than the fast (1 + 1) EA by a factor polynomial in k on Jumpk, is slower by a factor polynomial in n on some Jumpk,δ instances. Computationally, the new class allows experiments with wider fitness valleys, especially when they lie further away from the global optimum.

All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The number of calculations in these algorithms is generally proportional to population size. Classical genetic algorithms (GAs), for instance, require the calculation of the fitness values of every individual in the population. A new evolutionary algorithm that combines two basic operators of GAs: namely, selection and crossover are proposed. The new operator is applied to two randomly selected individuals from the existing population. It is shown that the number of calculations is reduced greatly compared to classical GAs, while performance is enhanced on the functions studied. Moreover, simulation studies indicate that the new algorithm can produce a performance comparable with the more intelligent and hybrid evolutionary techniques given in the related literature.

In this paper, a hybrid evolutionary algorithm (HEA) based on the approaches of the evolutionary algorithm and a local search (LS) is proposed to determine the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is one of the most common malignant bone tumor in children. The HEA consists of a population of individuals but the evolution of individuals is conducted by a LS, rather than the crossover and mutation used in the traditional evolutionary algorithms. The proposed HEA can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. Experimental results indicate that HEA can obtain more accurate signatures than the other existing approaches in determining chemoresponse for osteosarcoma.

One promising method of improving Evolutionary Algorithm (EA) performance is to improve its fine tuning capabilities by using an additional local optimization operator in the evolutionary cycle. This customization on the traditional EA is typically called a Memetic Algorithm (MA). Adding the appropriate local optimization algorithm can increase performance, while a poor choice can decrease performance. Thus, some knowledge is required to select the correct algorithm. In many cases the optimal algorithm selection is not known and it may not be static, but could change during evolution. This investigation combines a method of local optimizer evolution using Push Genetic Programming with a method of automatic, self-configuration called Supportive Coevolution. This combination creates a novel MA that coevolves local optimization operators with target fitness function solution candidates. Implementation methodology is shown and experimentation details with corresponding results are presented. Some additional parameters that were discovered for performance tuning are discussed along with a study of their impact on the algorithm's performance. Discussion of some interesting insights followed by some suggestions for further investigation are also provided. Results show the proposed technique can improve the performance of an EA by providing automatically configured, coevolved local optimization operators to a MA.

We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the $$(1+\lambda )$$ evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the $$(1+\lambda )$$ EA finds the optimum in an expected optimization time (number of fitness evaluations) of $$O(n\lambda /\log \lambda +n\log n)$$ . This time is asymptotically smaller than the optimization time of the classic $$(1+\lambda )$$ EA. Previous work shows that this performance is best-possible among all $$\lambda $$ -parallel mutation-based unbiased black-box algorithms. This result shows that the new way of adjusting the mutation rate can find optimal dynamic parameter values on the fly. Since our adjustment mechanism is simpler than the ones previously used for adjusting the mutation rate and does not have parameters itself, we are optimistic that it will find other applications.

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS’s unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction.

Mutations that accumulate in the human male germline with age are a major driver of genetic diversity and contribute to genetic diseases. However, aging-related male germline mutation rates have not been measured directly in germline cells (sperm) at the level of individuals. We developed a study design in which we recalled 23 sperm donors with prior banked samples to provide new sperm samples. The old and new sequential sperm samples were separated by long timespans, ranging from 10 to 33 years. We profiled these samples by high-fidelity duplex sequencing and demonstrate that direct high-fidelity sequencing of sperm yields cohort-wide mutation rates and patterns consistent with prior family-based (trio) studies. In every individual, we detected an increase in sperm mutation burden between the two sequential samples, yielding individual-specific measurements of germline mutation rate. Deep whole-genome sequencing of sequential sperm samples from two individuals followed by targeted validation measured remarkably stable mosaicism of clonal mutations that likely arose during embryonic and germline development, suggesting that age did not substantially impact the diversity of spermatogonial stem cell pools in these individuals. Our application of high-fidelity and deep whole-genome sequencing to sequential sperm samples provides insight into aging-related mutation processes in the male germline.

Virulence Optimization Algorithm

In this paper, a new multi-objective evolutionary algorithm for solving high complex multi-objective problems is presented based on the rule of energy minimizing and the law of entropy increasing of particle systems in phase space, Through the experiments it proves that this algorithm can quickly obtains the Pareto solutions with high precision and uniform distribution. And the results of the experiments show that this algorithm can avoid the premature phenomenon of problems better than the traditional evolutionary algorithm because it can drive all the individuals to participate in the evolving operation in each generation.

Adaptive search for broad attention based vision transformers

A semiclassical surface hopping model is presented for the calculation of nonadiabatic transition probabilities for the case in which the avoided crossing point is in the classically forbidden regions. The exact potentials and coupling are replaced with simple functional forms that are fitted to the values, evaluated at the turning point in the classical motion, of the Born-Oppenheimer potentials, the nonadiabatic coupling, and their first few derivatives. For the one-dimensional model considered, reasonably accurate results for transition probabilities are obtained down to around 10(-10). The possible extension of this model to many dimensional problems is discussed. The fact that the model requires only information at the turning point, a point that the trajectories encounter would be a significant advantage in many dimensional problems over Landau-Zener type models, which require information at the avoided crossing seam, which is in the forbidden region where the trajectories do not go.

university is a complicated work so in the implementation it have violation of the constraints and it also takes a lot of time since it is created manually. In this paper evolutionary algorithm (EA) is used to create an effective and feasible schedules based on the real data input that is obtained from each department. The objective functions in EA contribute in gaining the fitness function to solve the constraints problem in the schedule by applying weighting for each hard constraints. The objective function is gained from the total of infringement in each soft constraints addition by score weighting. The genetic operator used in EA is stochastic variation Operator. As far as the reproduction operator is concerned, the tournament selection was used with size 3. Crossover operator is conducted after selection process with crossover probability equal to 0.05 and mutation rate is 0.1. The size of population was set to 9 and stopping criteria algorithm was left run for fitness value = 1. The simulation result shows that EA can create lecture schedules efficiently and feasibly. Moreover, it is also faster with the execution time of the proposed EA is less than 30 and easier than creating manually.

In chemical engineering, reliable models are necessary to reduce the cost of process design. An evolutionary algorithm with resizable population was used to estimate coefficients of Sovova's mass transfer model and was compared with a global optimiser found in the literature and commonly used differential evolution algorithm. Comparison of the evolutionary algorithm to the global optimisation technique proved that the evolutionary algorithm is more robust, efficient, and significantly better than the global optimiser in regards to the deviation of the model from experimental data. It is also shown that the proposed evolutionary algorithm performed better than differential evolution algorithm.