The fixed charge transportation problem (FCTP) is a classic challenge for combinatorial optimization; it is based on the well-known transportation problem (TP), and is one of the prime examples of an NP-complete variant of the TP, of general importance in a wide range of transportation network design problems. Many techniques have been applied to this problem, and the most effective so far (in terms of near-optimal results in reasonable time on large instances) are evolutionary algorithm based approaches. In particular, an EA proposed by Eckert and Gottlieb has produced the best performance so far on a set of specific benchmark instances. We introduce a new scheme, which has more general applicability, but which we test here on the FCTP. The proposed scheme applies an adaptive mutation process immediately following the evaluation of a phenotype. It thereby adapts automatically to learned information encoded in the chromosome. The underlying encoding approach is to encode an ordering of elements for interpretation by a constructive algorithm (such as with the Link and Node Biased encoding for spanning trees, and the Random Keys encoding which has been applied to both scheduling and graph problems), however the main adaptive process rewards links in such a way that genes effectively encode a measure of the number of times their associated link has appeared in selected solutions. Tests are done which compare our approach with Eckert and Gottlieb's results on benchmark FCTP instances, and other approaches.

In orthogonal frequency-division multiplex access (OFDMA) uplink, the carrier-frequency offsets (CFOs) between the multiple transmitters and the receiver introduce inter-carrier interference (ICI) and severely degrade the performance. In this paper, based on the perfect estimation of each user's CFO, we propose two low-complexity iterative algorithms to cancel ICI due to CFOs, which are denoted as the basic algorithm and the improved algorithm with decision-feedback equalization (DFE), respectively. For the basic one, two theorems are proposed that yield a sufficient condition for the convergence of iterations. Moreover, the interference-power-evolution (IPE) charts are proposed to evaluate the convergence behavior of this interference cancellation algorithm. Motivated by the IPE chart, the procedure of DFE is introduced into the iterations, which is the basic idea of the improved algorithm. For this improved algorithm, the error-propagation effect are analyzed and suppressed by an efficient stopping criterion. From IPE charts and simulation results, it can be easily observed that the basic algorithm has the same capability of ICI cancellation as the linear optimal minimum mean square error (MMSE) method, but offers lower complexity, while the improved algorithm with DFE outperforms the MMSE method in terms of the bit-error rate (BER) performance.

Decoding performance of LDPC (Low-Density Parity-Check) codes is highly dependent on the degree distributions of the Tanner graphs which define the LDPC codes. We compare two LDPC code ensembles, one has a uniform degree distribution and the other a non-uniform one over a BEC (Binary Erasure Channel) and a BSC (Binary Symmetric Channel) thorough DE (Density Evolution). We then derive sufficient conditions on the erasure probability of a BEC and the error probability of a BSC, under which the LDPC code ensembles with uniform degree distributions outperform those with non-uniform degree distributions.

The Evolutionary Electronics refers to the design method of electronic circuits with the help of Evolutionary Algorithms. Over the years huge experience has been accumulated and tremendous results have been achieved in this field. Two obvious tendencies are prevailing in the area over designers to improve the performance of Evolutionary Algorithms. First of all, as with any solution-search-algorithm, the designers try to reduce the potential solution space in order to reach the optimum solution faster, putting some constrains onto search algorithm as well as onto potential solutions. At the same time, the second tendency of unconstraining the Evolutionary Algorithms in its search gives unpredictable breakthroughs in results. Enabling the evolution to optimize with more experimental parameters devoted to drive the evolution and adjusted previously manually, is one of an example where such kind of unconstraining takes place. The evolution with the maximum freedom of search can be addressed as unconstrained evolution. The unconstrained evolution has already been applied in the past towards the design of digital circuits, and extraordinary results have been obtained, including generation of circuits with smaller number of electronic components. Recently, the similar method has been introduced by authors of this paper towards the design of analogue circuits. The new algorithm has produced promising results in terms of quality of the circuits evolved and evolutionary resources required. It differed from constrained method by its simplicity and represented one of the first attempts to apply Evolutionary Strategy towards the analogue circuit design. In this paper both conventional constrained evolution and newly developed unconstrained evolution in analogue domain are compared in detail on the example of "LC" low-pass filter design. The unconstrained evolution demonstrates the superior behaviour over the constrained one and exceeds by quality of results the best filter evolved previously by 240%. The experimental results are presented along with detailed analysis. Also, the obtained results are compared in details with low-pass filters designed previously.

Software defined radio, which uses reconfigurable signal processing devices, requires the determination of multiple unknown parameters to realize the potential capabilities of adaptive communication. Evolutional algorithms are optimal multi dimensional search techniques, and are well known to be effective for parameter determination. This letter proposes an evolutional algorithm for learning the mobile time-varying channel parameters without any specific assumption of scattering distribution. The proposed method is very simple to realize, but can provide precise channel estimation results. Simulations of an OFDM system show that for an example of OFDM communication under the time-varying fading channel, the proposed learning method can achieve the better BER performance.

Irregular Repeat-Accumulate (IRA) codes, introduced by Jin et al., have a linear-time encoding algorithm and their decoding performance is comparable to that of irregular low-density parity-check (LDPC) codes. Meanwhile the authors have introduced detailedly represented irregular LDPC code ensembles specified with joint degree distributions between variable nodes and check nodes. In this paper, by using density evolution method [7],[8], we optimize IRA codes specified with joint degree distributions. Resulting codes have higher thresholds than Jin's IRA codes.

This paper considers Cyclic Job-Shop Scheduling Problems (CJSSP) extended from the Job-Shop Scheduling Problem (JSSP). We propose an evolutionary computing method to solve the problem approximately by generating the Petri net structure for scheduling. The crossover proposed in this paper employs structural analysis of Petri net model, that is, the crossover improves the cycle time by breaking the bottle-neck circuit obtained by solving a linear programming problem. Experimental evaluation shows the effectiveness of our approach.

This paper deals with the Optimum Communication Spanning Tree Problem (OCST) which is well known as an NP-hard problem. For solving the problem, we uses an evolutionary approach. This paper presents a new effective tree encoding and proposes a tree construction routine (TCR) to generate a tree from the encoding. The basic principle is to break a cycle. We also propose a new crossover operator that focuses on the inheritance of parental information and the use of network information. Consequently, we confirm that the proposed algorithm is superior to other algorithms applied to the OCST problem or other tree problems. Moreover, our method can find a better solution than the solution which was previously known as the best solution. In addition, we analyzed the locality and diversity property of encoding and observed that the proposed method has high locality and at the same time it preserves population diversity for many generations. Finally, we conclude that these properties are the main reasons why the proposed method outperforms the other encodings.

If some differences of perceptions arise between populations, then strategies which are regarded as the same strategy in a population may be perceived distinguishably in the other populations. To discuss such a situation, replicator dynamics for multi-population hypergames has been proposed. However, it is assumed that players' perceptions are given and fixed. In this paper, we consider that each population has various interpretation functions and choose one of them depending on payoffs, and we propose a hybrid system representation of replicator dynamics with changes of interpretation functions. Moreover, we apply our proposed model to a well-known example of a hypergame "Soccer Hooliganism" and show that behaviors converging to heteroclinic orbits can appear by the changes of the interpretation functions.

In evolutionary game theory, to the best of our knowledge, individuals' perceptions have not been taken into consideration explicitly. When an individual interacts with the other individual under coexistence of heterogeneous sub-populations, the individual may be willing to change his/her strategy depending on the sub-population the other individual belongs to. Moreover, in such a situation, each individual may make an error about the sub-population the other individual belongs to. In this paper, we propose a multi-population model with such erroneous perceptions. We define an evolutionarily stable strategy (ESS) and formulate replicator dynamics in this model, and prove several properties of the proposed model. Moreover, we focus on a two-population chicken game with erroneous perceptions and discuss characteristics of equilibrium points of its replicator dynamics.

A general method for generating multiple two-dimensional frequency-hopping pilot signals with limited mutual interference, for propagation channel estimation in time and frequency with equidistant sampling, is presented. Each pilot signal uses a different generic frequency-hopping pilot pattern that is repeated in frequency domain, with repetition period equal to the desired sampling interval in frequency domain. Some interesting special cases of the general construction are considered as well. The practical applicability and usefulness of the proposed solution are demonstrated by the numerical evaluation of a set of frequency-hopping pilot patterns in a typical multi-cell scenario of the future evolved third generation cellular systems.

This paper presents a performance and thresholds for Irregular Tree-LDPC codes. We obtain optimal irregular degree distributions and threshold by the density evolution technique. It is presented that Irregular Tree-LDPC code has performance gain at low SNR.

In this paper, the performance of Tree-LDPC code [1] is presented based on the min-sum algorithm with scaling and the asymptotic performance in the water fall region is shown by density evolution. We presents that the Tree-LDPC code show a significant performance gain by scaling with the optimal scaling factor [3] which is obtained by density evolution methods. We also show that the performance of min-sum with scaling is as good as the performance of sum-product while the decoding complexity of min-sum algorithm is much lower than that of sum-product algorithm.

An efficient clustering strategy for estimation of distribution algorithms (EDAs) is presented. It is used for properly fitting probabilistic models that play an important role in guiding search direction. To this end, a fitness-aided ordering scheme is devised for deciding the input sequence of samples (i.e., individuals) for clustering. It can effectively categorise the individuals by using the (available) information about fitness landscape. Moreover, a virtual leader is introduced for providing a reliable reference for measuring the distance from samples to its own cluster. The proposed algorithm incorporates them within the framework of random the leader algorithm (RLA). Experimental results demonstrate that the proposed approach is more effective than the existing ones with regard to probabilistic model fitting.

A novel evolutionary algorithm is described for designing the topology of spanning tree-based communication networks. Two specific performance objectives are dealt with: the optimum communication spanning tree problem (OCSTP), and the quadratic minimum spanning tree problem (q-MST). Improved network performance is reliably obtained when using the proposed algorithm on accepted benchmark instances, in comparison with the previous best-known approaches. The same methodology can be applied straightforwardly to the design of communication networks with other objectives.

This paper proposes an efficient two-phase optimization approach for a compact W-band double-plane stepped rectangular waveguide filter design, which combines genetic algorithms (GAs) with the simplified transmission-line model and full-wave analysis. Being more efficient and robust than the gradient-based method, the approach can lead to a compact waveguide filter design. Numerical results show that the resultant waveguide filter design with 4 sections (total length 19.6 mm) is sufficient to meet the design goal and provides comparable performance to that with 8 sections (total length 35.6 mm) by the Chebyshev synthesis approach. Based on the present approach, nineteen compact high-pass waveguide filters have been implemented and measured at the W-band with satisfactory performance.

Wireless Internet technologies have been developing and users are now able to access more information anywhere through small screen mobile devices. However, due to the limits of cost, bandwidth and screen size in a wireless environment, it is important to minimize interactions between a mobile user and his handheld device, as well as the amount of data transmitted. In this paper we present an interactive evolutionary approach for user-oriented Web search by using mobile devices. To verify this approach, a series of experiments has been conducted. The results show that our approach can allocate the information a user needs within only a few user-system interactions. It substantially reduces the number of retrieved pages a user has to visit. This is especially an important benefit to mobile users.

Effective trading with given pattern-based multi-predictors of stock price needs an intelligent asset allocation strategy. In this paper, we study a method of dynamic asset allocation, called the meta policy, which decides how much the proportion of asset should be allocated to each recommendation for trade. The meta policy makes a decision considering both the recommending information of multi-predictors and the current ratio of stock funds over the total asset. We adopt evolutionary computation to optimize the meta policy. The experimental results on the Korean stock market show that the trading system with the proposed meta policy outperforms other systems with fixed asset allocation methods.

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the face search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to handle it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experiments on a set of 42 test images show the effectiveness of the proposed approach. Moreover, the effect of PSO parameter settings on the search performance was investigated.

In this work we give an extension of Kauffman's NK-Landscapes to multiobjective MNK-Landscapes in order to study the effects of epistasis on the performance of multiobjective evolutionary algorithms (MOEAs). This paper focuses on the development of multiobjective random one-bit climbers (moRBCs). We incrementally build several moRBCs and analyze basic working principles of state of the art MOEAs on landscapes of increased epistatic complexity and number of objectives. We specially study the effects of Pareto dominance, non-dominance, and the use of memory and a population to influence the search. We choose an elitist non-dominated sorting multiobjective genetic algorithm (NSGA-II) as a representative of the latest generation of MOEAs and include its results for comparison. We detail the behavior of the climbers and show that population based moRBCs outperform NSGA-II for all values of M and K.