Mean Running Time Research Articles

Study on steel plate scratch detection based on improved MSR and phase consistency

In the casting process of the steel plate, due to the influence of rolling equipment and technology, the defects such as cracks and scratches appear on the surface of steel plate, which affect the performance of steel plate and even cause production accidents. In this paper, an automatic detection method for steel plate scratch is proposed. Firstly, the steel plate image is decomposed by channel and the enhanced image is obtained by the improved MSR (Multi-Scale Retinex) enhancement algorithm. Then, the phase consistency is detected after the Log Gabor wavelet transform and the scratch areas are obtained by the threshold segmentation and intersection of three channels. Finally, the scratch position is identified and the scratch characteristics such as width and length can be calculated. The results show that the minimum error of the characteristics measurement is only 2.28% in the experimental environment and 4.15% in the field environment, and the mean running time is 0.2826 s in the experimental environment and 0.3193 s in the field environment. It verifies that the proposed method is effective and practical.

Superdiffusion in self-reinforcing run-and-tumble model with rests.

This paper introduces a run-and-tumble model with self-reinforcing directionality and rests. We derive a single governing hyperbolic partial differential equationfor the probability density of random-walk position, from which we obtain the second moment in the long-time limit. We find the criteria for the transition between superdiffusion and diffusion caused by the addition of a rest state. The emergence of superdiffusion depends on both the parameter representing the strength of self-reinforcement and the ratio between mean running and resting times. The mean running time must be at least 2/3 of the mean resting time for superdiffusion to be possible. Monte Carlo simulations validate this theoretical result. This work demonstrates the possibility of extending the telegrapher's (or Cattaneo) equationby adding self-reinforcing directionality so that superdiffusion occurs even when rests are introduced.

Outcomes of surgical treatment of Stanford type of aortic dissection at Hanoi Heart Hospital in the period 2015-2020

Background: Stanford type A aortic dissection is a complex disease and a serious surgical emergency. The diagnosis is determined based on diagnostic imagines. Surgery is the mainstay of treatment, with high mortality and morbidity. This study aimed to comment on clinical and paraclinical characteristics and evaluate the results of surgical treatment of type A aortic dissection at Hanoi Heart Hospital within 5 years from 2015 to 2020.
 Methods: Retrospective and descriptive study of patients diagnosed with type A aortic dissection who were surgically treated at Hanoi Heart Hospital from January 2015 to May 2020.
 Results: 109 cases were included in the study with the mean age of 56.0 ± 14.4; 50 - 60 age accounted for the highest percentage (30.3%); men accounted for 67.9%. Classic type A aortic dissection in 95 cases (87.2%). Chest pain was the main clinical symptom (91.7%); 4.6% came to the hospital in a state of cardiogenic shock and circulatory arrest. The rate of Marfan phenotype was 13.8%. The most common surgery is replacing the ascending aorta (45.9%); total replacement of the aortic arch accounted for 17.4%; Total root replacement and aortic arch accounted for 3.7%. The average aortic pairing time was 120.7 ± 41.0 minutes, the mean running time was 179.7 ± 57.0 minutes. Re-operative bleeding accounted for 6,4%; cerebrovascular accident accounted for 2,8%. The early mortality rate after surgery was 9.2%. The mean follow-up time was 24.93 ± 16.13 months, the mean survival time was 52.0 ± 1.9 months, the survival rate was 88.1% after 1 year and 85.3% after 5 years.
 Conclusion: Early mortality, postoperative complications and survival rate after follow-up were positive with surgical technique and conditions of anesthesia and resuscitation at Hanoi Heart Hospital.

Multi-Stage Power-Aware Intelligent Adaptive Routing Algorithms in Bundled Links Based Backbone Networks

The increasing energy consumption in backbone networks has been one of crucial concerns in Information and Communications Technology (ICT) sector. From a viewpoint of intelligent adaption, we propose Green Routing in Backbone networks with Bundled links, which is referred to as GRB2, to investigate the substantial power saving in a bundled link based backbone network for above enormous challenges from severe alarming statistics of the issues on economy, energy and environment. We formulate the above problems as Mixed Integer Linear Programming (MILP) models and develop power-aware greedy heuristics to solve them. We have investigated and compared the different characterizations of the solutions to the proposed problems by evaluating network power consumption including Power Saving Ratio (PSR) profile over time, PSR profile under different Maximum Cable Utilization (MCU) and PSR profile under different Bundled Sizes (BSs) and network performance including Powered-Off Cable Rate (POCR), Mean State Switching Times (MSST) and Mean Running Time (MRT) for various traffic demands during PPs and OPPs under different real backbone network topology scenarios compared with MSPF, SSPF, and HDEER. Experiment results show the different power-saving potential of these solutions once applied in the backbone network.

A Noise-Robust Partial Blur Detection Algorithm Combining Walsh Transform and Sequency Truncation

Blur detection is an important yet challenging task in computer vision. The previous algorithms are mostly difficult to achieve a cost-benefit balance and their performance is largely limited when faced with the blur image polluted by noise. To address these issues, a fast and unsupervised blur detection algorithm is proposed, which is robust to noise. First, a re-blur strategy and Walsh transform are utilized to analyze the input image in sequency domain. Meanwhile, the low-sequency zone of sequency spectrum is adaptively truncated to eliminate the noise interference. Then, a noise-robust local blur metric is constructed and pixel-wise blurriness is calculated to obtain the blur map. Finally, the blur region is segmented using the clustering-guided multi-scale growth framework. Experimental results on CUHK and DUT datasets demonstrate that the proposed algorithm can detect the image blur effectively and efficiently, and achieves the state-of-the-art performance on multiple indicators like precision, recall, <italic>F</italic>₁-measure, mean absolute error, and mean runtime. Especially on noise-polluted conditions, the proposed algorithm significantly surpasses other competitive algorithms.

Remote Sensing Image Registration based on a Geometrical Model Matching

Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods previously used in the remote sensing image registration. The defects of SIFT are the large number of mismatches and high execution time due to the high dimensions of classical SIFT descriptor. These drawbacks reduce the efficiency of the SIFT algorithm. To enhance the performance of the remote sensing image registration, this paper proposes an approach consisting of three different steps. At first, the keypoints of both reference and second images are extracted using SIFT algorithm. Then, to increase the speed of the algorithm and accuracy of the matching, the SIFT descriptor with the vector length of 64 is used for keypoints description. Finally, a new method has been proposed for the image matching. The proposed matching method is based on calculating the distances of keypoints and their transformed points. Simulation results of applying the proposed method to some standard databases demonstrated the superiority of this approach compared with some other existing methods, according to the root mean square error (RMSE), precision and running time criteria.

A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm.

Automated Large Artery Occlusion Detection in Stroke: A Single-Center Validation Study of an Artificial Intelligence Algorithm

Introduction: Expediting notification of lesions in acute ischemic stroke (AIS) is critical. Limited availability of experts to assess such lesions and delays in large vessel occlusion (LVO) recognition can negatively affect outcomes. Artificial intelligence (AI) may aid LVO recognition and treatment. This study aims to evaluate the performance of an AI-based algorithm for LVO detection in AIS. Methods: Retrospective analysis of a database of AIS patients admitted in a single center between 2014 and 2019. Vascular neurologists graded computed tomography angiographies (CTAs) for presence and site of LVO. Studies were analyzed by the Viz-LVO Algorithm® version 1.4 – neural network programmed to detect occlusions from the internal carotid artery terminus (ICA-T) to the Sylvian fissure. Comparisons between human versus AI-based readings were done by test characteristic analysis and Cohen’s kappa. Primary analysis included ICA-T and/or middle cerebral artery (MCA)-M1 LVOs versus non-LVOs/more distal occlusions. Secondary analysis included MCA-M2 occlusions. Results: 610 CTAs were analyzed. The AI algorithm rejected 2.5% of the CTAs due to poor quality, which were excluded from the analysis. Viz-LVO identified ICA-T and MCA-M1 LVOs with a sensitivity of 87.6%, specificity of 88.5%, and accuracy of 87.9% (AUC 0.88, 95% CI: 0.85–0.92, p < 0.001). Cohen’s kappa was 0.74. In the secondary analysis, the algorithm yielded a sensitivity of 80.3%, specificity of 88.5%, and accuracy of 82.7%. The mean run time of the algorithm was 2.78 ± 0.5 min. Conclusion: Automated AI reading allows for fast and accurate identification of LVO strokes with timely notification to emergency teams, enabling quick decision-making for reperfusion therapies or transfer to specialized centers if needed.

Effects of internal dynamics on chemotactic aggregation of bacteria

The effects of internal adaptation dynamics on the self-organized aggregation of chemotactic bacteria are investigated by Monte Carlo (MC) simulations based on a two-stream kinetic transport equation coupled with a reaction–diffusion equation of the chemoattractant that bacteria produce. A remarkable finding is a nonmonotonic behavior of the peak aggregation density with respect to the adaptation time; more specifically, aggregation is the most enhanced when the adaptation time is comparable to or moderately larger than the mean run time of bacteria. Another curious observation is the formation of a trapezoidal aggregation profile occurring at a very large adaptation time, where the biased motion of individual cells is rather hindered at the plateau regimes due to the boundedness of the tumbling frequency modulation. Asymptotic analysis of the kinetic transport system is also carried out, and a novel asymptotic equation is obtained at the large adaptation-time regime while the Keller–Segel type equations are obtained when the adaptation time is moderate. Numerical comparison of the asymptotic equations with MC results clarifies that trapezoidal aggregation is well described by the novel asymptotic equation, and the nonmonotonic behavior of the peak aggregation density is interpreted as the transient of the asymptotic solutions between different adaptation time regimes.

Multiple Kernel Boosting Saliency Detection of Flame Image of Sintering Section

<p indent=0mm>The flame image of the sintering section is usually interfered by smoke and halo, which would cause the blurring of flame edge and material layer in the image. In order to solve the problem that traditional saliency detection method based on two-dimensional image features is difficult to effectively obtain the actual saliency of the cross-sectional flame image, a saliency detection method based on boundary connectivity and multi-kernel Boosting is proposed. Firstly, the color de-correlation is used in the process of image color space conversion. Boundary connectivity and dark channel prior principle are used to obtain the initial saliency map. Secondly, the super-pixel region information, regional variance and regional contrast of the flame image are used to construct region descriptor, the multiple kernel Boosting algorithm based on support vector regression is used to generate the complementary saliency map on 4 scales. Finally, the initial saliency map and the complementary saliency map are fused to obtain the final saliency map. 600 flame images including manual labeling are used to compare the proposed method with other 5 existing methods and each stage of the proposed method is analyzed. <italic>P</italic>-<italic>R</italic> curve, <italic>F</italic>-measure, mean absolute error and running time are taken as evaluation indexes. The experimental results show that the proposed method is superior to the other 5 methods, and the detection performance of each stage is gradually enhanced, which lays a foundation for improving the effective information extraction of sintering flame section image.

Implementation of Not So Naive and Burkhard-Keller Tree algorithms in Indonesian-Hokkien dictionary application

In Indonesia, especially in Medan, North Sumatra, Hokkien is used in everyday life within the Chinese community. However, learning the language is still done manually. Therefore, the authors intend to create an Android-based Hokkien dictionary application. The most common typing errors found were deleting and inserting single characters, substituting and reversing adjacent characters. Exact string matching can perform a quick search, but the search will fail when the incorrect input is entered. This study aims to prove the advantages and disadvantages of each algorithm in accessing information in addition to displaying the results of the running time of both algorithms. The results showed that the search time for the Burkhard-Keller Tree algorithm increased by 72% and 96% for the Not So Naive algorithm when searching on a database containing 200 words and 400 words. The Not So Naive algorithm has a mean running time of 26.9 ms and a mean running time of 53.5 ms on the Burkhard-Keller Tree algorithm.

P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing.

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

TEAP: Traffic Engineering and ALR policy based Power-aware solutions for green routing and planning problems in backbone networks

Enormous and ever-increasing energy consumption in the Internet and the burgeoning global GreenHouse Gas (GHG) emission that come with it have been crucial issues for the past few years due to an exponential traffic growth and a rapid expansion of communication infrastructures worldwide. In this paper, we target Routing and Planning problems of Green Networking with bundled links referred to as RPGN by leveraging Traffic Engineering (TE) and Adaptive Link Rate (ALR) policy jointly to investigate the power-saving potentialities and effective applicability in the backbone networks. We formulate RPGN as a non-linear multi-commodity flow model and develop green heuristics-TE and ALR policy based Power-aware heuristics (TEAP) to solve it. We have investigated and compared different characterizations of the solutions to RPGN by evaluating network power saving ratio, rate level duration distribution, mean rate switching times and mean running time, under different real backbone network topology scenarios. Our results indicate the different power-saving potential of these solutions once applied in the backbone network.

Cat bond valuation using Monte Carlo and quasi Monte Carlo method

The frequency of natural disasters is very small, but the loss of damage and the risks that must be borne is of enormous value. Recently, Catastrophe bond (CAT bond) has grown rapidly in financial markets to increase the coverage of environmental disasters and the resulting economic losses, insurance companies and reinsurers are barely covered. Catastrophe bond (CAT Bond) is one of the insurance-linked financial securities instruments that aim to transfer risk by insuring natural disaster events to the capital market. The model of natural disaster loss without a jump-diffusion process followed a model of geometric Brownian motion. We estimate and match each parameter in each model by using grid search and then we use the method of Monte Carlo and quasi-Monte Carlo to obtain numerical results for the CAT bonds pricing formulas. From this simulation, the Monte Carlo method has good enough accuracy and efficiency to valuates the CAT bond. Less then 500 iterations Monte Carlo have reached convergence in 1280.49 seconds. And quasi-Monte Carlo more efficient with less than 400 iterations have reached convergence in 30.89 seconds. Both methods have reached small enough MAPE. Based on the simulations carried out through this research, it was showed that the quasi-Monte Carlo method is better than the Monte Carlo method based on the value of Mean Absolute Percentage Error (MAPE) and running time.

Hermite Interpolation Based Interval Shannon-Cosine Wavelet and Its Application in Sparse Representation of Curve

Using the wavelet transform defined in the infinite domain to process the signal defined in finite interval, the wavelet transform coefficients at the boundary are usually very large. It will bring severe boundary effect, which reduces the calculation accuracy. The construction of interval wavelet is the most common method to reduce the boundary effect. By studying the properties of Shannon-Cosine interpolation wavelet, an improved version of the wavelet function is proposed, and the corresponding interval interpolation wavelet based on Hermite interpolation extension and variational principle is designed, which possesses almost all of the excellent properties such as interpolation, smoothness, compact support and normalization. Then, the multi-scale interpolation operator is constructed, which can be applied to select the sparse feature points and reconstruct signal based on these sparse points adaptively. To validate the effectiveness of the proposed method, we compare the proposed method with Shannon-Cosine interpolation wavelet method, Akima method, Bezier method and cubic spline method by taking infinitesimal derivable function cos(x) and irregular piecewise function as an example. In the reconstruction of cos(x) and piecewise function, the proposed method reduces the boundary effect at the endpoints. When the interpolation points are the same, the maximum error, average absolute error, mean square error and running time are 1.20 × 10−4, 2.52 × 10−3, 2.76 × 10−5, 1.68 × 10−2 and 4.02 × 10−3, 4.94 × 10−4, 1.11 × 10−3, 9.27 × 10−3, respectively. The four indicators mentioned above are all lower than the other three methods. When reconstructing an infinitely derivable function, the curve reconstructed by our method is smoother, and it satisfies C2 and G2 continuity. Therefore, the proposed method can better realize the reconstruction of smooth curves, improve the reconstruction efficiency and provide new ideas to the curve reconstruction method.

Research on Short-Term Forecast of Power Load Based on Adam-Gru

To forecast the short-term power load accurately and reliably, a short-term power load model based on gate recurrent unit was proposed. In order to improve the accuracy of the model, the Adam algorithm is used to calculate the GRU hyperparameter Optimization, so as to construct an Adam-GRU short-term power load forecasting model. The performance of the forecasting model was evaluated by the Root Mean Square Error and running time, then the forecast results were compared with LSTM method. The experimental results show that Adam-GRU has a higher accuracy than the LSTM model, an increase of 15.54%, and a time complexity reduction of 15.75%. Compared with the RNN model, the accuracy has improved 10.84%. The results show that the short-term power load forecasting model and parameter optimization method based on Adam-GRU can effectively forecast the power load data.

Thou shalt not trust online videos for inguinal hernia repair techniques.

Videos are used by surgeons when learning new techniques; however, online videos are often not vetted. Our aim is to review online videos of laparoscopic inguinal hernia repairs based on a benchmark for critical view of the myopectineal orifice (MPO) and safe inguinal hernia repair as defined by Daes and Felix and commonly referred to as "the 9 Commandments." YouTubeⓇ was queried for "laparoscopic inguinal hernia repair." The top 50 videos were ranked based on number of views. Those endorsed and/or vetted by surgical societies were excluded (n = 4). Three expert hernia surgeons scored the videos based on adherence to the 9 Commandments. The 50 videos originated from 11 countries. They had 72,825 mean views and a mean runtime of 14min. Videos obeyed a median of 77.8% of commandments shown. Eight videos (16%) obeyed all 9 (100%) commandments. Three videos (6%) failed to obey any commandments. Operations employed TEP (18, 36%), TAPP (28, 56%), and rTAPP (4, 8%) approach. Stratification by approach showed significant variance in commandments obeyed (Kurskal-Wallis, p = 0.016) with significant difference between TEP and rTAPP scores (p = 0.008) and no significant difference between TEP and TAPP or rTAPP and TAPP scores. Twenty-three videos (46%) displayed unsafe techniques including: threatened critical structures (16, 32%), rough tissue handling (15, 30%), and dangerous placement of fixation (9, 18%). Online surgical videos on YouTube are not reliable in demonstrating best practices for minimally invasive inguinal hernia repairs. In our study, only 16% of the most viewed videos followed all 9 Commandments for critical view of the MPO. Many showed suboptimal repairs with significant safety concerns. While a significant number of online videos are a free and readily available resource for surgeons around the world, we recommend caution in relying on non-vetted videos as a form of surgical education.

Effect of bilateral lesions in the amygdala on the maximum running velocity and cardiovascular response in rats during treadmill running

The high intensity of exercise around/at maximum levels induces psychological distress. It is known that the amygdala in the limbic system is one of the regions of the brain controlling such emotion, as well as regulating the cardiovascular system. Therefore, we hypothesized that the amygdala could play an important role in limiting the high‐intensity exercise performance. To test this, we examined whether bilateral amygdala lesions increased maximal exercise performance with altered cardiovascular responses during exercise. The arterial pressure in rats was chronically measured using radio‐telemetry during treadmill running. The rats were subjected to forced running for 60 min per day (velocity ≤20 m/min) for 3 days to achieve treadmill habituation. Thereafter, an incremental exercise test was conducted until exhaustion (pre‐test). Exercise intensity was initiated at 10 m/min, and the velocity was increased by 2 m/min every 3 min. In the amygdala lesion group, the bilateral lesions in the amygdala were induced by electrical stimulation (direct current 1 mA for 5 s). In the sham group, the tip of the electrode was inserted into the amygdala and immediately removed without electrical stimulation. After surgery, re‐training was conducted similar to the habituation protocol, and the incremental exercise test was repeated (post‐test). In the sham group, there were no significant differences between pre‐ and post‐test in the mean maximum running velocities (pre‐test: 47.2 ± 1.8 m/min, post‐test: 44.0 ± 2.8 m/min) and running time (pre‐test: 56.5 ± 3.2 min; post‐test: 49.9 ± 4.7 min). However, the mean maximum running velocity was greater (pre‐test: 45.5 ± 2.3 m/min; post‐test: 54.0 ± 0.6 m/min; p &lt; 0.05) and the running time was longer (pre‐test: 57.9 ± 3.4 min; post‐test: 70.0 ± 0.8 min; p &lt; 0.05) in the post‐test as compared to the pre‐test of the lesion group. Moreover, we found that the mean arterial pressure in both groups of animals exhibited a gradual increase during submaximal exercise (low/moderate intensity) followed by a drastic increase during high/maximal‐intensity exercise. However, compared to the sham group, the mean arterial pressure response in the amygdala lesion group was significantly lower during submaximal exercise, whereas it was greater during high/maximal‐intensity exercise, suggesting that the amygdala could play a role in limiting the exercise performance by modulating the autonomic cardiovascular control.Support or Funding InformationGrant‐in‐Aid for JSPS Fellows (19J22706)

Ice Hockey-Specific Repeated Shuttle Sprint Test Performed on Ice Should Not Be Replaced by Off-Ice Testing.

Legerlotz, K, Kittelmann, J, Dietzel, M, Wolfarth, B, and Böhlke, N. Ice hockey-specific repeated shuttle sprint test performed on ice should not be replaced by off-ice testing. J Strength Cond Res 36(4): 1071-1076, 2022-Although the importance of sport-specific testing has been stated in various studies, the application of standard tests that are little related to the requirements in competition is still widespread in performance diagnostics. Furthermore, the actual exercise mode in testing often deviates from the exercise mode in competition. The aim of this study was therefore to investigate how the performance in an ice hockey mimicking repeated sprint shuttle test conducted off-ice (RSS) differs from the on-ice performance (RISS). The two performance tests were completed by 21 male junior ice hockey players within one week. Anaerobic fatigue was significantly larger in RISS and did not correlate with RSS, whereas best run, mean run, total run time, turn and fly time, and total times in all three shifts correlated moderately. Although the best and mean run times did not differ, these times were achieved with different strategies depending on the test condition, indicated by significantly different split times. Aerobic fatigue in shift 3 was the only parameter where the off-ice measurement correlated strongly with the on-ice measurement. Our results imply that an off-ice test does not predict on-ice performance with sufficient precision, strongly advocating performance testing in the exercise mode used in competition.

Frequency Fitness Assignment: Making Optimization Algorithms Invariant Under Bijective Transformations of the Objective Function Value

Under frequency fitness assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and is subject to minimization. FFA renders optimization processes invariant under bijective transformations of the objective function value. On TwoMax, Jump, and Trap functions of dimension s, the classical (1 + 1)-EA with standard mutation at rate 1/s can have expected runtimes exponential in s. In our experiments, a (1 + 1)-FEA, the same algorithm but using FFA, exhibits mean runtimes that seem to scale as s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>2</i></sup> ln s. Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three. On OneMax, LeadingOnes, and Plateau problems, it seems to be slower than the (1 + 1)-EA by a factor linear in s. The (1 + 1)-FEA performs much better than the (1 + 1)-EA on W-Model and MaxSat instances. We further verify the bijection invariance by applying the Md5 checksum computation as transformation to some of the above problems and yield the same behaviors. Finally, we show that FFA can improve the performance of a memetic algorithm for job shop scheduling.